
> ## Replication: "Backscratching in Banks: Political Cycles in Bank Manager Appointments."
> ## Robustness Analyses (in chronological order as appearing in appendix):
> ### -- replication code for all empirical analyses and plots in the online appendix of the paper
> ## R Version: 3.6.0 (2019-04-26)
> ## platform:  x86_64-apple-darwin15.6.0
> #######################################
> 
> rm(list = ls())

> # load packages
> library(eha)

> require(devtools)

> library(survival)

> library(flexsurv)

> library(stargazer)

> library(texreg)  

> library(dplyr)

> library(simPH)

> library(lfe)

> # set working directory
> ## -- set your working directory --
> 
> # load data
> cajas.boards <- read.table(file = "cajas_dta.txt", header = T
+   , stringsAsFactors = F)  # data for Spanish savings banks

> cbanks.election <- read.table(file = "commercial-banks_dta.txt", header = T
+   , stringsAsFactors = F)  # data for Spanish commercial banks

> set.seed(123)

> ###############################################################################
> ############## APPENDIX #######################################################
> ###############################################################################
> 
> # **Table A3: Summary Statistics**:  ------------------------------------------
> 
> ## generate dummies for different parties
> cajas.boards$social <- ifelse(cajas.boards$region.govParty == "Socialist", 1, 0)

> cajas.boards$conserv <- ifelse(cajas.boards$region.govParty == "Conservative", 1, 0)

> cajas.boards$other <- ifelse(cajas.boards$region.govParty == "Others", 1, 0)

> ## obtain summary statistics
> stargazer(as.data.frame(cajas.boards[cajas.boards$bank.chair == 1
+   ,c("region.postElec"
+     ,"region.govChange"
+     , "region.yrsInGov"
+     , "region.coal"
+     , "bank.roaLag1"
+     , "conserv"
+     , "social"
+     , "other"
+     , "bank.polVote")])
+   , title = "Summary Statistics"
+   , label = "T:sumstat", digits = 2
+   , omit.summary.stat = c("p25", "p75")
+   , covariate.labels = c("Post Election"
+     ,"Government Change"
+     , "Years in Government"
+     , "Coalition"
+     , "Return on Assets"
+     , "Party: Conservative"
+     , "Party: Socialist"
+     , "Party: Other"
+     , "Public Sector Vote Share")
+   , type = "text")

Summary Statistics
=======================================================
Statistic                  N   Mean St. Dev.  Min  Max 
-------------------------------------------------------
Post Election            1,259 0.52   0.50     0    1  
Government Change        1,259 0.35   0.48     0    1  
Years in Government      1,259 8.28   6.32     1    28 
Coalition                1,259 0.32   0.47     0    1  
Return on Assets         1,259 0.90   0.48   -2.32 3.67
Party: Conservative      1,259 0.31   0.46     0    1  
Party: Socialist         1,259 0.35   0.48     0    1  
Party: Other             1,259 0.33   0.47     0    1  
Public Sector Vote Share 1,259 0.45   0.11   0.31  0.75
-------------------------------------------------------

> ## get summary statistics for tenure (manual estimation of last two rows of table)
> round(summary(cajas.boards[cajas.boards$bank.chair ==1,]$bank.yrsInOffice), 2)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    2.00    3.00    4.51    6.00   20.00 

> round(sd(cajas.boards[cajas.boards$bank.chair ==1,]$bank.yrsInOffice), 2)
[1] 3.91

> round(summary(cajas.boards[cajas.boards$bank.chair ==0,]$bank.yrsInOffice), 2)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    2.00    4.00    5.71    8.00   24.00 

> round(sd(cajas.boards[cajas.boards$bank.chair ==0,]$bank.yrsInOffice), 2)
[1] 4.86

> # **Table A4**: Test of PH Assumption:  ---------------------------------------
> 
> 
> ## function for cox.zph to test proportionality of hazards
> 
> cox.zphFunc <- function (fit, transform = "km", global = TRUE) 
+ {
+   call <- match.call()
+   if (!inherits(fit, "coxph")) 
+     stop("Argument must be the result of coxph")
+   if (inherits(fit, "coxph.null")) 
+     stop("The are no score residuals for a Null model")
+   sresid <- resid(fit, "schoenfeld")
+   varnames <- names(fit$coefficients)
+   nvar <- length(varnames)
+   ndead <- length(sresid)/nvar
+   if (nvar == 1) 
+     times <- as.numeric(names(sresid))
+   else times <- as.numeric(dimnames(sresid)[[1]])
+   if (is.character(transform)) {
+     tname <- transform
+     ttimes <- switch(transform, identity = times, rank = rank(times), 
+       log = log(times), km = {
+         temp <- survfitKM(factor(rep(1, nrow(fit$y))), 
+           fit$y, se.fit = FALSE)
+         t1 <- temp$surv[temp$n.event > 0]
+         t2 <- temp$n.event[temp$n.event > 0]
+         km <- rep(c(1, t1), c(t2, 0))
+         if (is.null(attr(sresid, "strata"))) 1 - km else (1 - 
+             km[sort.list(sort.list(times))])
+       }, stop("Unrecognized transform"))
+   }
+   else {
+     tname <- deparse(substitute(transform))
+     if (length(tname) > 1) 
+       tname <- "user"
+     ttimes <- transform(times)
+   }
+   xx <- ttimes - mean(ttimes)
+   r2 <- sresid %*% fit$var * ndead
+   test <- xx %*% r2
+   corel <- c(cor(xx, r2))
+   z <- c(test^2/(diag(fit$var) * ndead * sum(xx^2)))
+   Z.ph <- cbind(corel, z, pchisq(z, 1, lower.tail = FALSE))
+   if (global && nvar > 1) {
+     test <- c(xx %*% sresid)
+     z <- c(test %*% fit$var %*% test) * ndead/sum(xx^2)
+     Z.ph <- rbind(Z.ph, c(NA, z, pchisq(z, ncol(sresid), 
+       lower.tail = FALSE)))
+     dimnames(Z.ph) <- list(c(varnames, "GLOBAL"), c("rho", 
+       "chisq", "p"))
+   }
+   else dimnames(Z.ph) <- list(varnames, c("rho", "chisq", "p"))
+   dimnames(r2) <- list(times, names(fit$coefficients))
+   temp <- list(table = Z.ph, x = ttimes, y = r2 + outer(rep(1, 
+     ndead), fit$coefficients), var = fit$var, call = call, 
+     transform = tname)
+   if (is.R()) 
+     class(temp) <- "cox.zph"
+   else oldClass(temp) <- "cox.zph"
+   temp
+ }

> ## Uncorrected Cox Model w/out Time and Frailties
> fit.basic <- coxph(Surv(bank.yrsInOffice, bank.event) ~  region.yrsInGov +
+     region.postElec +
+     region.coal + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty)  +
+     frailty(bank.id, distribution = "gaussian"),
+   data = cajas.boards)

> ## Grambsch-Thernau and Harroll's rho test for basic model
> ### this table appears as Table A4 in the appendix.
> phtest.basic <- cox.zphFunc(fit.basic)

> print(phtest.basic)
                                         rho   chisq       p
region.yrsInGov                      0.11336  7.6365 0.00572
region.postElec                     -0.03557  0.5321 0.46573
region.coal                          0.04703  1.1780 0.27776
bank.polVote                         0.03294  1.4427 0.22970
bank.roaLag1                        -0.00802  0.0491 0.82468
as.factor(region.govParty)Others    -0.02050  0.7164 0.39731
as.factor(region.govParty)Socialist -0.05641  3.7861 0.05168
GLOBAL                                    NA 14.3990 0.04452

> ### estimates differ slightly from the submitted manuscript
> ### substantive conclusions are not affected
> ### this was discussed and accepted by the editor
> 
> ## account for violation by interacting with measure of time
> fit.corrected <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov +
+     region.postElec +
+     region.coal + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice  +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards)

> ## Grambsch-Thernau and Harroll's rho test for corrected model
> phtest <- cox.zphFunc(fit.corrected)

> print(phtest)
                                                             rho    chisq      p
region.yrsInGov                                          0.05979 1.85e+00 0.1737
region.postElec                                         -0.00119 5.74e-04 0.9809
region.coal                                              0.02543 2.97e-01 0.5857
bank.polVote                                            -0.00477 2.00e-02 0.8876
bank.roaLag1                                            -0.02625 4.18e-01 0.5181
as.factor(region.govParty)Others                        -0.06774 3.83e+00 0.0504
as.factor(region.govParty)Socialist                     -0.06854 3.07e+00 0.0795
as.factor(region.govParty)Conservative:bank.yrsInOffice -0.15374 9.01e-03 0.9244
as.factor(region.govParty)Others:bank.yrsInOffice        0.01781 1.07e-04 0.9917
as.factor(region.govParty)Socialist:bank.yrsInOffice     0.01838 9.99e-05 0.9920
GLOBAL                                                        NA 9.89e+00 0.4502

> # Table A5: exclude early/repeated elections ----------------------------------
> 
> ## exclude early and repeated elections (identified separately, see footnote 12 in Appendix)
> cajas.boards.rstrct <- subset(cajas.boards,
+   !(cajas.boards$region.id == 13 & cajas.boards$year >= 2003 & cajas.boards$year < 2007) 
+   & !(cajas.boards$region.id == 9 & cajas.boards$year >= 1995 & cajas.boards$year < 1999)
+   & !(cajas.boards$region.id == 9 & cajas.boards$year >= 2006 & cajas.boards$year < 2010)
+   & !(cajas.boards$region.id == 16 & cajas.boards$year >= 1986 & cajas.boards$year < 1990)
+   & !(cajas.boards$region.id == 16 & cajas.boards$year >= 2001 & cajas.boards$year < 2005)
+   & !(cajas.boards$region.id == 1 & cajas.boards$year >= 1996 & cajas.boards$year < 2000))

> ## Column 1
> fit0.earlyElec <- coxph(Surv(bank.yrsInOffice, bank.event) ~  region.yrsInGov,
+   method = "efron",
+   data = cajas.boards.rstrct, subset = bank.chair == 1)

> summary(fit0.earlyElec)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov, 
    data = cajas.boards.rstrct, subset = bank.chair == 1, method = "efron")

  n= 1115, number of events= 212 

                    coef exp(coef) se(coef)      z Pr(>|z|)    
region.yrsInGov -0.05699   0.94461  0.01180 -4.828 1.38e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov    0.9446      1.059     0.923    0.9667

Concordance= 0.607  (se = 0.02 )
Likelihood ratio test= 26.61  on 1 df,   p=2e-07
Wald test            = 23.31  on 1 df,   p=1e-06
Score (logrank) test = 24.03  on 1 df,   p=9e-07


> ## Column 2
> fit1.earlyElec <- coxph(Surv(bank.yrsInOffice, bank.event) ~  region.yrsInGov +
+     region.coal + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards.rstrct, subset = bank.chair == 1)

> summary(fit1.earlyElec)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + bank.polVote + bank.roaLag1 + as.factor(region.govParty) + 
    as.factor(region.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cajas.boards.rstrct, subset = bank.chair == 
    1, method = "efron")

  n= 1115, number of events= 212 

                          coef     se(coef) se2     Chisq DF    p      
region.yrsInGov           -0.06123 0.01603  0.01508 14.58  1.00 1.3e-04
region.coal               -0.20730 0.18721  0.17935  1.23  1.00 2.7e-01
bank.polVote               0.25068 0.89083  0.76380  0.08  1.00 7.8e-01
bank.roaLag1               0.05871 0.14829  0.14157  0.16  1.00 6.9e-01
as.factor(region.govParty -0.21978 0.34242  0.30970  0.41  1.00 5.2e-01
as.factor(region.govParty  0.68748 0.29029  0.26937  5.61  1.00 1.8e-02
frailty(bank.id, distribu                           52.23 30.42 8.2e-03
as.factor(region.govParty -7.13674 1.51424  1.51416 22.21  1.00 2.4e-06
as.factor(region.govParty -7.18008 1.51408  1.51399 22.49  1.00 2.1e-06
as.factor(region.govParty -7.23753 1.51408  1.51402 22.85  1.00 1.8e-06

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           0.9406036     1.0631 9.115e-01   0.97063
region.coal               0.8127723     1.2304 5.631e-01   1.17307
bank.polVote              1.2849004     0.7783 2.242e-01   7.36459
bank.roaLag1              1.0604708     0.9430 7.930e-01   1.41816
as.factor(region.govParty 0.8026928     1.2458 4.103e-01   1.57044
as.factor(region.govParty 1.9887044     0.5028 1.126e+00   3.51292
as.factor(region.govParty 0.0007953  1257.3257 4.089e-05   0.01547
as.factor(region.govParty 0.0007616  1313.0193 3.917e-05   0.01481
as.factor(region.govParty 0.0007191  1390.6521 3.698e-05   0.01398

Iterations: 8 outer, 160 Newton-Raphson
     Variance of random effect= 0.2938953 
Degrees of freedom for terms=  0.9  0.9  0.7  0.9  1.7 30.4  2.8 
Concordance= 0.943  (se = 0.943 )
Likelihood ratio test= 717.5  on 38.34 df,   p=<2e-16


> ## Column 3
> fit2.earlyElec <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov +
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards.rstrct, subset = bank.chair == 1)

> summary(fit2.earlyElec)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards.rstrct, 
    subset = bank.chair == 1, method = "efron")

  n= 1115, number of events= 212 

                          coef     se(coef) se2     Chisq  DF    p      
region.yrsInGov           -0.05736 0.01595  0.01517  12.93  1.00 3.2e-04
region.coal               -0.15948 0.18681  0.17954   0.73  1.00 3.9e-01
region.postElec            0.42727 0.14805  0.14747   8.33  1.00 3.9e-03
bank.polVote               0.38783 0.84400  0.74815   0.21  1.00 6.5e-01
bank.roaLag1               0.03416 0.14596  0.14017   0.05  1.00 8.1e-01
as.factor(region.govParty -0.28205 0.33526  0.31144   0.71  1.00 4.0e-01
as.factor(region.govParty  0.87927 0.28289  0.26640   9.66  1.00 1.9e-03
frailty(bank.id, distribu                            33.79 22.58 6.1e-02
as.factor(region.govParty -7.98170 0.64446  0.64424 153.39  1.00 3.1e-35
as.factor(region.govParty -8.02377 0.64390  0.64369 155.28  1.00 1.2e-35
as.factor(region.govParty -8.11152 0.64428  0.64409 158.51  1.00 2.4e-36

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           0.9442551     1.0590 9.152e-01  0.974248
region.coal               0.8525859     1.1729 5.912e-01  1.229555
region.postElec           1.5330695     0.6523 1.147e+00  2.049179
bank.polVote              1.4737751     0.6785 2.818e-01  7.706333
bank.roaLag1              1.0347529     0.9664 7.773e-01  1.377449
as.factor(region.govParty 0.7542393     1.3258 3.910e-01  1.455064
as.factor(region.govParty 2.4091437     0.4151 1.384e+00  4.194309
as.factor(region.govParty 0.0003417  2926.8932 9.661e-05  0.001208
as.factor(region.govParty 0.0003276  3052.6695 9.273e-05  0.001157
as.factor(region.govParty 0.0003001  3332.6460 8.488e-05  0.001061

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.1813305 
Degrees of freedom for terms=  0.9  0.9  1.0  0.8  0.9  1.7 22.6  2.8 
Concordance= 0.938  (se = 0.938 )
Likelihood ratio test= 702.1  on 31.69 df,   p=<2e-16


> screenreg(list(fit0.earlyElec, fit1.earlyElec,fit2.earlyElec),
+   caption = "Cox PH Model excluding irregular elections", caption.above = T,
+   label="T:exclIrregElec",
+   custom.coef.names = c("Years in Government"
+     , "Coalition"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist", "Post Election"),
+   custom.model.names = c("Raw Model", "Full Model 1", "Full Model 2"),
+   include.rsquared=F, include.maxrs=F, include.missings=F, include.zph=F,
+   reorder.coef = c(1:2,7,3:6),
+   stars = c(.01,.05,.1),
+   omit.coef = c("bank.yrsInOffice"))

=================================================================
                          Raw Model    Full Model 1  Full Model 2
-----------------------------------------------------------------
Years in Government         -0.06 ***    -0.06 ***     -0.06 *** 
                            (0.01)       (0.02)        (0.02)    
Coalition                                -0.21         -0.16     
                                         (0.19)        (0.19)    
Post Election                                           0.43 *** 
                                                       (0.15)    
Public Sector Vote Share                  0.25          0.39     
                                         (0.89)        (0.84)    
Return on Assets$_{t-1}$                  0.06          0.03     
                                         (0.15)        (0.15)    
Party: Other                             -0.22         -0.28     
                                         (0.34)        (0.34)    
Party: Socialist                          0.69 **       0.88 *** 
                                         (0.29)        (0.28)    
-----------------------------------------------------------------
AIC                       2599.53      1983.26       1985.46     
Num. events                212          212           212        
Num. obs.                 1115         1115          1115        
=================================================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> ### estimates in model 2 differ slightly from the submitted manuscript
> ### substantive conclusions are not affected
> ### this was discussed and accepted by the editor
> 
> 
> # **Table A6**: CEO only ------------------------------------------------------
> 
> ## Column 1
> fit.ceoMod1 <- coxph(Surv(bank.yrsInOffice, bank.event) ~  region.yrsInGov +
+     region.coal + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 0)

> summary(fit.ceoMod1)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + bank.polVote + bank.roaLag1 + as.factor(region.govParty) + 
    as.factor(region.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cajas.boards, subset = bank.chair == 
    0, method = "efron")

  n= 1259, number of events= 165 

                          coef       se(coef) se2     Chisq  DF    p      
region.yrsInGov           -0.0227772 0.0180   0.01691   1.60  1.00 2.1e-01
region.coal               -0.0001915 0.2202   0.21345   0.00  1.00 1.0e+00
bank.polVote               0.8388754 1.1509   0.97624   0.53  1.00 4.7e-01
bank.roaLag1              -0.2476055 0.1839   0.17565   1.81  1.00 1.8e-01
as.factor(region.govParty  0.4968639 0.4161   0.35311   1.43  1.00 2.3e-01
as.factor(region.govParty  0.5749019 0.3810   0.34358   2.28  1.00 1.3e-01
frailty(bank.id, distribu                              34.60 45.33 8.8e-01
as.factor(region.govParty -5.5667367 0.3881   0.38758 205.77  1.00 1.1e-46
as.factor(region.govParty -5.5947220 0.3882   0.38781 207.65  1.00 4.5e-47
as.factor(region.govParty -5.5698095 0.3885   0.38814 205.59  1.00 1.3e-46

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov            0.977480     1.0230  0.943593  1.012585
region.coal                0.999808     1.0002  0.649393  1.539310
bank.polVote               2.313764     0.4322  0.242484 22.077736
bank.roaLag1               0.780668     1.2810  0.544382  1.119513
as.factor(region.govParty  1.643559     0.6084  0.727121  3.715041
as.factor(region.govParty  1.776956     0.5628  0.842147  3.749433
as.factor(region.govParty  0.003823   261.5791  0.001787  0.008179
as.factor(region.govParty  0.003717   269.0029  0.001737  0.007956
as.factor(region.govParty  0.003811   262.3841  0.001780  0.008161

Iterations: 10 outer, 200 Newton-Raphson
     Variance of random effect= 0.816717 
Degrees of freedom for terms=  0.9  0.9  0.7  0.9  1.5 45.3  2.8 
Concordance= 0.959  (se = 0.959 )
Likelihood ratio test= 640.5  on 53.08 df,   p=<2e-16


> ## Column 2
> fit.ceoMod2 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov +
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 0)

> summary(fit.ceoMod2)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 0, method = "efron")

  n= 1259, number of events= 165 

                          coef     se(coef) se2     Chisq DF    p      
region.yrsInGov           -0.02610 0.01846  0.01722  2.00  1.00 1.6e-01
region.coal               -0.04605 0.22405  0.21716  0.04  1.00 8.4e-01
region.postElec           -0.04043 0.16713  0.16650  0.06  1.00 8.1e-01
bank.polVote               0.56951 1.15023  0.98362  0.25  1.00 6.2e-01
bank.roaLag1              -0.22137 0.18773  0.17835  1.39  1.00 2.4e-01
as.factor(region.govParty  0.51791 0.43213  0.36213  1.44  1.00 2.3e-01
as.factor(region.govParty  0.58257 0.39510  0.35137  2.17  1.00 1.4e-01
frailty(bank.id, distribu                           86.96 44.49 1.5e-04
as.factor(region.govParty -4.53852 0.81424  0.81410 31.07  1.00 2.5e-08
as.factor(region.govParty -4.57165 0.81400  0.81392 31.54  1.00 2.0e-08
as.factor(region.govParty -4.53912 0.81391  0.81385 31.10  1.00 2.4e-08

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov             0.97424     1.0264  0.939615   1.01014
region.coal                 0.95499     1.0471  0.615584   1.48153
region.postElec             0.96038     1.0413  0.692118   1.33261
bank.polVote                1.76740     0.5658  0.185462  16.84285
bank.roaLag1                0.80142     1.2478  0.554705   1.15788
as.factor(region.govParty   1.67852     0.5958  0.719612   3.91521
as.factor(region.govParty   1.79063     0.5585  0.825466   3.88431
as.factor(region.govParty   0.01069    93.5520  0.002167   0.05273
as.factor(region.govParty   0.01034    96.7040  0.002097   0.05098
as.factor(region.govParty   0.01068    93.6081  0.002167   0.05266

Iterations: 10 outer, 200 Newton-Raphson
     Variance of random effect= 0.7922662 
Degrees of freedom for terms=  0.9  0.9  1.0  0.7  0.9  1.5 44.5  2.7 
Concordance= 0.963  (se = 0.963 )
Likelihood ratio test= 697.6  on 53.11 df,   p=<2e-16


> ## Column 3
> fit.ceoMod3a <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.coal + 
+     region.postElec +
+     region.coal:region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 0 & region.govChange == 1)

> summary(fit.ceoMod3a)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.coal + 
    region.postElec + region.coal:region.postElec + bank.polVote + 
    bank.roaLag1 + as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 0 & region.govChange == 1, method = "efron")

  n= 445, number of events= 59 

                          coef    se(coef) se2    Chisq DF    p      
region.coal                0.4543 0.4993   0.4894  0.83  1.00 3.6e-01
region.postElec            0.1544 0.5349   0.5304  0.08  1.00 7.7e-01
bank.polVote               2.5358 1.5932   1.4530  2.53  1.00 1.1e-01
bank.roaLag1              -0.5984 0.3225   0.3104  3.44  1.00 6.3e-02
as.factor(region.govParty  0.7089 0.5585   0.5223  1.61  1.00 2.0e-01
as.factor(region.govParty -0.6892 0.7419   0.7013  0.86  1.00 3.5e-01
frailty(bank.id, distribu                         15.52 11.05 1.6e-01
region.coal:region.postEl -0.1756 0.6259   0.6208  0.08  1.00 7.8e-01
as.factor(region.govParty -5.8326 0.9203   0.9196 40.17  1.00 2.3e-10
as.factor(region.govParty -5.9425 0.9217   0.9209 41.57  1.00 1.1e-10
as.factor(region.govParty -5.8459 0.9204   0.9197 40.34  1.00 2.1e-10

                          exp(coef) exp(-coef) lower .95 upper .95
region.coal                1.575035     0.6349 0.5919761   4.19060
region.postElec            1.166954     0.8569 0.4090063   3.32949
bank.polVote              12.626603     0.0792 0.5561135 286.68804
bank.roaLag1               0.549676     1.8193 0.2921703   1.03413
as.factor(region.govParty  2.031778     0.4922 0.6800084   6.07069
as.factor(region.govParty  0.501958     1.9922 0.1172600   2.14874
region.coal:region.postEl  0.838915     1.1920 0.2459860   2.86105
as.factor(region.govParty  0.002930   341.2526 0.0004826   0.01779
as.factor(region.govParty  0.002626   380.8678 0.0004312   0.01599
as.factor(region.govParty  0.002892   345.8079 0.0004762   0.01756

Iterations: 5 outer, 100 Newton-Raphson
     Variance of random effect= 0.3193096 
Degrees of freedom for terms=  1.0  1.0  0.8  0.9  1.7 11.0  1.0  2.9 
Concordance= 0.965  (se = 0.965 )
Likelihood ratio test= 224.8  on 20.33 df,   p=<2e-16


> ## Column 4
> fit.ceoMod3b <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.coal + 
+     region.postElec +
+     region.coal:region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 0 & region.govChange == 0)

> summary(fit.ceoMod3b)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.coal + 
    region.postElec + region.coal:region.postElec + bank.polVote + 
    bank.roaLag1 + as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 0 & region.govChange == 0, method = "efron")

  n= 814, number of events= 106 

                          coef     se(coef) se2    Chisq  DF   p      
region.coal                0.07899 0.4385   0.4225   0.03  1.0 8.6e-01
region.postElec            0.02501 0.2289   0.2271   0.01  1.0 9.1e-01
bank.polVote              -1.49271 1.3847   1.2466   1.16  1.0 2.8e-01
bank.roaLag1              -0.22980 0.2160   0.2061   1.13  1.0 2.9e-01
as.factor(region.govParty -0.61461 0.5639   0.5023   1.19  1.0 2.8e-01
as.factor(region.govParty  1.68256 0.4773   0.4306  12.43  1.0 4.2e-04
frailty(bank.id, distribu                           29.83 20.6 8.6e-02
region.coal:region.postEl -0.06548 0.5928   0.5896   0.01  1.0 9.1e-01
as.factor(region.govParty -6.71140 0.3395   0.3388 390.80  1.0 5.5e-87
as.factor(region.govParty -6.66383 0.3378   0.3374 389.15  1.0 1.3e-86
as.factor(region.govParty -6.79351 0.3375   0.3372 405.22  1.0 4.0e-90

                          exp(coef) exp(-coef) lower .95 upper .95
region.coal                1.082195     0.9240 0.4581814  2.556075
region.postElec            1.025327     0.9753 0.6547018  1.605761
bank.polVote               0.224763     4.4491 0.0148965  3.391304
bank.roaLag1               0.794694     1.2583 0.5204365  1.213479
as.factor(region.govParty  0.540850     1.8489 0.1790836  1.633417
as.factor(region.govParty  5.379288     0.1859 2.1108587 13.708516
region.coal:region.postEl  0.936620     1.0677 0.2930737  2.993296
as.factor(region.govParty  0.001217   821.7183 0.0006256  0.002367
as.factor(region.govParty  0.001276   783.5446 0.0006583  0.002474
as.factor(region.govParty  0.001121   892.0428 0.0005786  0.002172

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.3415906 
Degrees of freedom for terms=  0.9  1.0  0.8  0.9  1.6 20.6  1.0  2.8 
Concordance= 0.959  (se = 0.959 )
Likelihood ratio test= 403  on 29.62 df,   p=<2e-16


> screenreg(list(fit.ceoMod1, fit.ceoMod2, fit.ceoMod3a, fit.ceoMod3b),
+   caption = "Cox PH Model, Bank CEOs Only", caption.above = T,
+   label="T:findingsCeo",
+   custom.coef.names = c("Years in Government"
+     , "Coalition"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist"
+     , "Post Election"
+     , "Post Election * Coalition"),
+   custom.model.names = c("Full Model 1", "Full Model 2", "After Gov't Change", "No Gov't Change"),
+   include.rsquared=F, include.maxrs=F, include.missings=F, include.zph=F,
+   reorder.coef = c(1:2,7:8,3:6),
+   stars = c(.01,.05,.1),
+   omit.coef = c("bank.yrsInOffice"))

==========================================================================================
                           Full Model 1  Full Model 2  After Gov't Change  No Gov't Change
------------------------------------------------------------------------------------------
Years in Government          -0.02         -0.03                                          
                             (0.02)        (0.02)                                         
Coalition                    -0.00         -0.05         0.45                0.08         
                             (0.22)        (0.22)       (0.50)              (0.44)        
Post Election                              -0.04         0.15                0.03         
                                           (0.17)       (0.53)              (0.23)        
Post Election * Coalition                               -0.18               -0.07         
                                                        (0.63)              (0.59)        
Public Sector Vote Share      0.84          0.57         2.54               -1.49         
                             (1.15)        (1.15)       (1.59)              (1.38)        
Return on Assets$_{t-1}$     -0.25         -0.22        -0.60 *             -0.23         
                             (0.18)        (0.19)       (0.32)              (0.22)        
Party: Other                  0.50          0.52         0.71               -0.61         
                             (0.42)        (0.43)       (0.56)              (0.56)        
Party: Socialist              0.57          0.58        -0.69                1.68 ***     
                             (0.38)        (0.40)       (0.74)              (0.48)        
------------------------------------------------------------------------------------------
AIC                        1541.61       1484.52       439.48              894.22         
Num. events                 165           165           59                 106            
Num. obs.                  1259          1259          445                 814            
==========================================================================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> ### estimates in model 2 differ slightly from the submitted manuscript
> ### substantive conclusions are not affected
> ### this was discussed and accepted by the editor
> 
> # **Table A7**: federal elections ---------------------------------------------
> 
> ## Column 1
> fit.FedChair <- coxph(Surv(bank.yrsInOffice, bank.event) ~ fed.yrsInGov + 
+     fed.postElec + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(fed.govParty) +  as.factor(fed.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1)

> summary(fit.FedChair)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ fed.yrsInGov + 
    fed.postElec + bank.polVote + bank.roaLag1 + as.factor(fed.govParty) + 
    as.factor(fed.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cajas.boards, subset = bank.chair == 
    1, method = "efron")

  n= 1259, number of events= 234 

                          coef     se(coef) se2     Chisq DF    p      
fed.yrsInGov               0.02959 0.02137  0.02121  1.92  1.00 1.7e-01
fed.postElec              -0.02992 0.14085  0.14048  0.05  1.00 8.3e-01
bank.polVote               1.31661 0.82663  0.65993  2.54  1.00 1.1e-01
bank.roaLag1               0.11394 0.14344  0.13718  0.63  1.00 4.3e-01
as.factor(fed.govParty)So  0.73582 0.29197  0.28689  6.35  1.00 1.2e-02
frailty(bank.id, distribu                           60.81 34.57 3.8e-03
as.factor(fed.govParty)Co -6.93444 1.14562  1.14554 36.64  1.00 1.4e-09
as.factor(fed.govParty)So -7.02432 1.14526  1.14519 37.62  1.00 8.6e-10

                          exp(coef) exp(-coef) lower .95 upper .95
fed.yrsInGov              1.0300277     0.9708 0.9877843  1.074078
fed.postElec              0.9705273     1.0304 0.7364061  1.279081
bank.polVote              3.7307663     0.2680 0.7381973 18.854873
bank.roaLag1              1.1206824     0.8923 0.8460323  1.484493
as.factor(fed.govParty)So 2.0871972     0.4791 1.1777112  3.699033
as.factor(fed.govParty)Co 0.0009737  1027.0474 0.0001031  0.009195
as.factor(fed.govParty)So 0.0008900  1123.6357 0.0000943  0.008399

Iterations: 7 outer, 140 Newton-Raphson
     Variance of random effect= 0.3055287 
Degrees of freedom for terms=  1.0  1.0  0.6  0.9  1.0 34.6  2.0 
Concordance= 0.946  (se = 0.946 )
Likelihood ratio test= 799.7  on 41.03 df,   p=<2e-16


> ## Column 2
> fit.FedCEO <- coxph(Surv(bank.yrsInOffice, bank.event) ~ fed.yrsInGov + 
+     fed.postElec + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(fed.govParty) + as.factor(fed.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 0)

> summary(fit.FedCEO)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ fed.yrsInGov + 
    fed.postElec + bank.polVote + bank.roaLag1 + as.factor(fed.govParty) + 
    as.factor(fed.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cajas.boards, subset = bank.chair == 
    0, method = "efron")

  n= 1259, number of events= 165 

                          coef     se(coef) se2    Chisq  DF    p      
fed.yrsInGov               0.02766 0.02559  0.0254   1.17  1.00 2.8e-01
fed.postElec               0.11412 0.16685  0.1664   0.47  1.00 4.9e-01
bank.polVote               1.75784 1.11726  0.8693   2.48  1.00 1.2e-01
bank.roaLag1              -0.22205 0.18455  0.1762   1.45  1.00 2.3e-01
as.factor(fed.govParty)So  0.58747 0.36194  0.3531   2.63  1.00 1.0e-01
frailty(bank.id, distribu                           39.33 48.76 8.3e-01
as.factor(fed.govParty)Co -5.52463 0.39447  0.3941 196.14  1.00 1.5e-44
as.factor(fed.govParty)So -5.58621 0.39391  0.3936 201.12  1.00 1.2e-45

                          exp(coef) exp(-coef) lower .95 upper .95
fed.yrsInGov               1.028045     0.9727  0.977750  1.080928
fed.postElec               1.120888     0.8921  0.808233  1.554489
bank.polVote               5.799915     0.1724  0.649249 51.812199
bank.roaLag1               0.800877     1.2486  0.557793  1.149897
as.factor(fed.govParty)So  1.799439     0.5557  0.885217  3.657840
as.factor(fed.govParty)Co  0.003987   250.7933  0.001840  0.008639
as.factor(fed.govParty)So  0.003749   266.7235  0.001732  0.008114

Iterations: 10 outer, 200 Newton-Raphson
     Variance of random effect= 0.9237069 
Degrees of freedom for terms=  1.0  1.0  0.6  0.9  1.0 48.8  2.0 
Concordance= 0.961  (se = 0.961 )
Likelihood ratio test= 641.5  on 55.16 df,   p=<2e-16


> screenreg(list(fit.FedChair, fit.FedCEO),
+   caption = "Placebo Test using Federal Elections", caption.above = T,
+   custom.coef.names = c("Years in Government"
+     , "Post General Election"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Socialist"),
+   custom.model.names = c("Chairpersons", "CEOs"),
+   include.rsquared=F, include.maxrs=F, include.missings=F, include.zph=F,
+   stars = c(.01,.05,.1),
+   omit.coef = c("bank.yrsInOffice"))

================================================
                          Chairpersons  CEOs    
------------------------------------------------
Years in Government          0.03          0.03 
                            (0.02)        (0.03)
Post General Election       -0.03          0.11 
                            (0.14)        (0.17)
Public Sector Vote Share     1.32          1.76 
                            (0.83)        (1.12)
Return on Assets$_{t-1}$     0.11         -0.22 
                            (0.14)        (0.18)
Party: Socialist             0.74 **       0.59 
                            (0.29)        (0.36)
------------------------------------------------
AIC                       2236.76       1544.74 
Num. events                234           165    
Num. obs.                 1259          1259    
================================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> ### estimates in model 2 differ slightly from the submitted manuscript
> ### substantive conclusions are not affected
> ### this was discussed and accepted by the editor
> 
> # **Table A8**: Placebo Test using commercial banks ---------------------------
> 
> ## Column 1
> fit.cbanksMod1 <- coxph(Surv(bank.yrsInOffice, bank.event) ~  region.yrsInGov,
+   method = "efron",
+   data = cbanks.election, subset = bank.chair == 1)

> summary(fit.cbanksMod1)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov, 
    data = cbanks.election, subset = bank.chair == 1, method = "efron")

  n= 176, number of events= 24 

                   coef exp(coef) se(coef)     z Pr(>|z|)
region.yrsInGov 0.05469   1.05622  0.03752 1.458    0.145

                exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov     1.056     0.9468    0.9813     1.137

Concordance= 0.551  (se = 0.062 )
Likelihood ratio test= 2.02  on 1 df,   p=0.2
Wald test            = 2.13  on 1 df,   p=0.1
Score (logrank) test = 2.16  on 1 df,   p=0.1


> ## Column 2
> fit.cbanksMod2 <- coxph(Surv(bank.yrsInOffice, bank.event) ~  region.yrsInGov +
+     region.coal + 
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cbanks.election, subset = bank.chair == 1)

> summary(fit.cbanksMod2)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + bank.roaLag1 + as.factor(region.govParty) + 
    as.factor(region.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cbanks.election, subset = bank.chair == 
    1, method = "efron")

  n= 176, number of events= 24 

                          coef     se(coef) se2     Chisq DF   p    
region.yrsInGov            0.02853 0.06091  0.05685 0.22  1.00 0.640
region.coal               -0.22474 0.91521  0.82337 0.06  1.00 0.810
bank.roaLag1              -0.14593 0.06926  0.06633 4.44  1.00 0.035
as.factor(region.govParty  2.35480 1.70028  1.57115 1.92  1.00 0.170
as.factor(region.govParty  3.43843 1.79857  1.73797 3.65  1.00 0.056
frailty(bank.id, distribu                           6.57  4.76 0.230
as.factor(region.govParty -5.27772 3.79848  3.79823 1.93  1.00 0.160
as.factor(region.govParty -5.51310 3.79458  3.79450 2.11  1.00 0.150
as.factor(region.govParty -5.47416 3.79845  3.79791 2.08  1.00 0.150

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov            1.028936    0.97188 9.131e-01    1.1594
region.coal                0.798724    1.25200 1.329e-01    4.8020
bank.roaLag1               0.864216    1.15712 7.545e-01    0.9899
as.factor(region.govParty 10.535972    0.09491 3.762e-01  295.0881
as.factor(region.govParty 31.138044    0.03212 9.169e-01 1057.4003
as.factor(region.govParty  0.005104  195.92247 2.983e-06    8.7334
as.factor(region.govParty  0.004034  247.91952 2.375e-06    6.8493
as.factor(region.govParty  0.004194  238.45116 2.451e-06    7.1755

Iterations: 5 outer, 100 Newton-Raphson
     Variance of random effect= 0.6782989 
Degrees of freedom for terms= 0.9 0.8 0.9 1.6 4.8 2.9 
Concordance= 0.972  (se = 0.972 )
Likelihood ratio test= 100.2  on 11.86 df,   p=4e-16


> ## Column 3
> fit.cbanksMod3 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov +
+     region.coal + 
+     region.postElec + 
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cbanks.election, subset = bank.chair == 1)

> summary(fit.cbanksMod3)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.roaLag1 + as.factor(region.govParty) + 
    as.factor(region.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cbanks.election, subset = bank.chair == 
    1, method = "efron")

  n= 176, number of events= 24 

                          coef     se(coef) se2     Chisq DF   p    
region.yrsInGov            0.01656 0.06220  0.05829 0.07  1.00 0.790
region.coal               -0.28905 0.89347  0.80594 0.10  1.00 0.750
region.postElec           -0.39878 0.46848  0.46156 0.72  1.00 0.390
bank.roaLag1              -0.14849 0.06916  0.06630 4.61  1.00 0.032
as.factor(region.govParty  2.39421 1.65921  1.53467 2.08  1.00 0.150
as.factor(region.govParty  3.51470 1.76551  1.70559 3.96  1.00 0.047
frailty(bank.id, distribu                           6.15  4.59 0.250
as.factor(region.govParty -5.43689 4.07395  4.07373 1.78  1.00 0.180
as.factor(region.govParty -5.66387 4.07102  4.07094 1.94  1.00 0.160
as.factor(region.govParty -5.65023 4.07411  4.07358 1.92  1.00 0.170

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov            1.016697    0.98358 9.000e-01    1.1485
region.coal                0.748975    1.33516 1.300e-01    4.3151
region.postElec            0.671137    1.49001 2.679e-01    1.6811
bank.roaLag1               0.862005    1.16009 7.527e-01    0.9871
as.factor(region.govParty 10.959582    0.09124 4.241e-01  283.2166
as.factor(region.govParty 33.605763    0.02976 1.056e+00 1069.5893
as.factor(region.govParty  0.004353  229.72710 1.483e-06   12.7802
as.factor(region.govParty  0.003469  288.26066 1.188e-06   10.1268
as.factor(region.govParty  0.003517  284.35575 1.197e-06   10.3282

Iterations: 5 outer, 100 Newton-Raphson
     Variance of random effect= 0.6446106 
Degrees of freedom for terms= 0.9 0.8 1.0 0.9 1.6 4.6 2.9 
Concordance= 0.97  (se = 0.97 )
Likelihood ratio test= 99.63  on 12.66 df,   p=1e-15


> screenreg(list(fit.cbanksMod1, fit.cbanksMod2, fit.cbanksMod3),
+   caption = "Cox PH Model using 16 commercial banks as a placebo", caption.above = T,
+   custom.coef.names = c("Years in Government"
+     , "Coalition"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist"
+     , "Post Election"),
+   custom.model.names = c("Raw Model", "Full Model 1", "Full Model 2"),
+   include.rsquared=F, include.maxrs=F, include.missings=F, include.zph=F,
+   stars = c(.01,.05,.1),
+   reorder.coef = c(1,6,2:5),
+   omit.coef = c("bank.yrsInOffice"))

===============================================================
                          Raw Model  Full Model 1  Full Model 2
---------------------------------------------------------------
Years in Government         0.05       0.03          0.02      
                           (0.04)     (0.06)        (0.06)     
Post Election                                       -0.40      
                                                    (0.47)     
Coalition                             -0.22         -0.29      
                                      (0.92)        (0.89)     
Return on Assets$_{t-1}$              -0.15 **      -0.15 **   
                                      (0.07)        (0.07)     
Party: Other                           2.35          2.39      
                                      (1.70)        (1.66)     
Party: Socialist                       3.44 *        3.51 **   
                                      (1.80)        (1.77)     
---------------------------------------------------------------
AIC                       208.88     132.47        134.60      
Num. events                24         24            24         
Num. obs.                 176        176           176         
===============================================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> # **Table A9**: FE models w/ region and year Fixed Effects --------------------
> 
> ## Column 1
> fit.fe0 <- felm(bank.event ~ region.yrsInGov +
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) | 0 | 0 | 0
+   , data = cajas.boards, subset = bank.chair == 1, exactDOF=TRUE)

> summary(fit.fe0)

Call:
   felm(formula = bank.event ~ region.yrsInGov + region.coal + region.postElec +      bank.polVote + bank.roaLag1 + as.factor(region.govParty) |      0 | 0 | 0, data = cajas.boards, exactDOF = TRUE, subset = bank.chair ==      1) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.29799 -0.21156 -0.17031 -0.09719  0.96224 

Coefficients:
                                     Estimate Std. Error t value Pr(>|t|)   
(Intercept)                          0.213065   0.068055   3.131  0.00178 **
region.yrsInGov                     -0.006707   0.002103  -3.190  0.00146 **
region.coal                         -0.017928   0.027853  -0.644  0.51991   
region.postElec                      0.047558   0.022341   2.129  0.03348 * 
bank.polVote                         0.019460   0.118423   0.164  0.86950   
bank.roaLag1                        -0.003598   0.023462  -0.153  0.87815   
as.factor(region.govParty)Others    -0.024860   0.028767  -0.864  0.38765   
as.factor(region.govParty)Socialist  0.033970   0.028082   1.210  0.22663   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3862 on 1251 degrees of freedom
Multiple R-squared(full model): 0.0204   Adjusted R-squared: 0.01492 
Multiple R-squared(proj model): 0.0204   Adjusted R-squared: 0.01492 
F-statistic(full model):3.721 on 7 and 1251 DF, p-value: 0.0005398 
F-statistic(proj model): 3.721 on 7 and 1251 DF, p-value: 0.0005398 



> ## Column 2
> fit.fe1 <- felm(bank.event ~ region.yrsInGov +
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) | region.id | 0 | region.id
+   , data = cajas.boards, subset = bank.chair == 1, exactDOF=TRUE)

> summary(fit.fe1)

Call:
   felm(formula = bank.event ~ region.yrsInGov + region.coal + region.postElec +      bank.polVote + bank.roaLag1 + as.factor(region.govParty) |      region.id | 0 | region.id, data = cajas.boards, exactDOF = TRUE,      subset = bank.chair == 1) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.36936 -0.21893 -0.15606 -0.07438  0.99081 

Coefficients:
                                     Estimate Cluster s.e. t value Pr(>|t|)  
region.yrsInGov                     -0.006640     0.002466  -2.693    0.016 *
region.coal                         -0.038037     0.038053  -1.000    0.332  
region.postElec                      0.044568     0.026887   1.658    0.117  
bank.polVote                        -0.177588     0.330676  -0.537    0.599  
bank.roaLag1                        -0.004669     0.032261  -0.145    0.887  
as.factor(region.govParty)Others    -0.045133     0.039082  -1.155    0.265  
as.factor(region.govParty)Socialist  0.084466     0.056250   1.502    0.153  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3851 on 1235 degrees of freedom
Multiple R-squared(full model): 0.03863   Adjusted R-squared: 0.02072 
Multiple R-squared(proj model): 0.0168   Adjusted R-squared: -0.001509 
F-statistic(full model, *iid*):2.157 on 23 and 1235 DF, p-value: 0.001222 
F-statistic(proj model):  8.11 on 7 and 16 DF, p-value: 0.000282 



> ## Column 3
> fit.fe2 <- felm(bank.event ~ region.yrsInGov +
+     region.coal +
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) | year | 0 | region.id
+   , data = cajas.boards, subset = bank.chair == 1, exactDOF=TRUE)

> summary(fit.fe2)

Call:
   felm(formula = bank.event ~ region.yrsInGov + region.coal + region.postElec +      bank.polVote + bank.roaLag1 + as.factor(region.govParty) |      year | 0 | region.id, data = cajas.boards, exactDOF = TRUE,      subset = bank.chair == 1) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.46585 -0.22144 -0.14162 -0.04944  0.97236 

Coefficients:
                                     Estimate Cluster s.e. t value Pr(>|t|)  
region.yrsInGov                     -0.001685     0.002226  -0.757   0.4492  
region.coal                          0.029625     0.030099   0.984   0.3252  
region.postElec                      0.033614     0.032251   1.042   0.2975  
bank.polVote                         0.206463     0.116178   1.777   0.0758 .
bank.roaLag1                        -0.047895     0.021051  -2.275   0.0231 *
as.factor(region.govParty)Others    -0.028721     0.027161  -1.057   0.2905  
as.factor(region.govParty)Socialist -0.003159     0.034090  -0.093   0.9262  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.382 on 1227 degrees of freedom
Multiple R-squared(full model): 0.06026   Adjusted R-squared: 0.03652 
Multiple R-squared(proj model): 0.01158   Adjusted R-squared: -0.0134 
F-statistic(full model, *iid*):2.538 on 31 and 1227 DF, p-value: 8.272e-06 
F-statistic(proj model): 3.713 on 7 and 16 DF, p-value: 0.01412 



> ## Column 4
> fit.fe3 <- felm(bank.event ~ region.yrsInGov
+   | year + region.id | 0 | region.id
+   , data = cajas.boards, subset = bank.chair == 1, exactDOF=TRUE)

> summary(fit.fe3)

Call:
   felm(formula = bank.event ~ region.yrsInGov | year + region.id |      0 | region.id, data = cajas.boards, exactDOF = TRUE, subset = bank.chair ==      1) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.44294 -0.21954 -0.14713 -0.03466  0.97331 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)
region.yrsInGov -0.001639     0.002752  -0.596     0.56

Residual standard error: 0.3818 on 1217 degrees of freedom
Multiple R-squared(full model): 0.06866   Adjusted R-squared: 0.03729 
Multiple R-squared(proj model): 0.0004097   Adjusted R-squared: -0.03327 
F-statistic(full model, *iid*):2.188 on 41 and 1217 DF, p-value: 2.848e-05 
F-statistic(proj model): 0.3549 on 1 and 16 DF, p-value: 0.5597 



> ## Column 5
> fit.fe4 <- felm(bank.event ~ region.yrsInGov +
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) | year + region.id | 0 | region.id
+   , data = cajas.boards, subset = bank.chair == 1, exactDOF=TRUE)

> summary(fit.fe4)

Call:
   felm(formula = bank.event ~ region.yrsInGov + region.coal + region.postElec +      bank.polVote + bank.roaLag1 + as.factor(region.govParty) |      year + region.id | 0 | region.id, data = cajas.boards, exactDOF = TRUE,      subset = bank.chair == 1) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.47945 -0.21849 -0.14358 -0.03302  0.98942 

Coefficients:
                                     Estimate Cluster s.e. t value Pr(>|t|)  
region.yrsInGov                     -0.001839     0.003052  -0.603    0.555  
region.coal                          0.002686     0.030086   0.089    0.930  
region.postElec                      0.029674     0.033441   0.887    0.388  
bank.polVote                        -0.037604     0.333297  -0.113    0.912  
bank.roaLag1                        -0.045665     0.023516  -1.942    0.070 .
as.factor(region.govParty)Others    -0.069513     0.059461  -1.169    0.260  
as.factor(region.govParty)Socialist  0.026268     0.057157   0.460    0.652  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3818 on 1211 degrees of freedom
Multiple R-squared(full model): 0.0734   Adjusted R-squared: 0.03744 
Multiple R-squared(proj model): 0.005498   Adjusted R-squared: -0.0331 
F-statistic(full model, *iid*):2.041 on 47 and 1211 DF, p-value: 5.591e-05 
F-statistic(proj model): 8.331 on 7 and 16 DF, p-value: 0.0002414 



> ## Column 6
> fit.fe5 <- felm(bank.event ~ region.yrsInGov
+   | year + region.id | 0 | region.id
+   , data = cajas.boards, subset = bank.chair == 1 & bank.mergerSubject != 1
+   , exactDOF=TRUE)

> summary(fit.fe5)

Call:
   felm(formula = bank.event ~ region.yrsInGov | year + region.id |      0 | region.id, data = cajas.boards, exactDOF = TRUE, subset = bank.chair ==      1 & bank.mergerSubject != 1) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.40025 -0.18454 -0.13154 -0.04542  0.99291 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)   
region.yrsInGov -0.004727     0.001599  -2.957  0.00928 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3609 on 973 degrees of freedom
Multiple R-squared(full model): 0.05501   Adjusted R-squared: 0.01519 
Multiple R-squared(proj model): 0.003951   Adjusted R-squared: -0.03802 
F-statistic(full model, *iid*):1.381 on 41 and 973 DF, p-value: 0.05781 
F-statistic(proj model): 8.742 on 1 and 16 DF, p-value: 0.009281 



> ## Column 7
> fit.fe6 <- felm(bank.event ~ region.yrsInGov +
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) | year + region.id | 0 | region.id
+   , data = cajas.boards, subset = bank.chair == 1 & bank.mergerSubject != 1
+   , exactDOF=TRUE)

> summary(fit.fe6)

Call:
   felm(formula = bank.event ~ region.yrsInGov + region.coal + region.postElec +      bank.polVote + bank.roaLag1 + as.factor(region.govParty) |      year + region.id | 0 | region.id, data = cajas.boards, exactDOF = TRUE,      subset = bank.chair == 1 & bank.mergerSubject != 1) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.48364 -0.18926 -0.13159 -0.03689  0.99076 

Coefficients:
                                     Estimate Cluster s.e. t value Pr(>|t|)   
region.yrsInGov                     -0.005563     0.001715  -3.244  0.00508 **
region.coal                         -0.012756     0.026437  -0.483  0.63598   
region.postElec                      0.005668     0.033172   0.171  0.86646   
bank.polVote                         0.043790     0.284382   0.154  0.87955   
bank.roaLag1                        -0.046984     0.038208  -1.230  0.23659   
as.factor(region.govParty)Others    -0.093339     0.048906  -1.909  0.07443 . 
as.factor(region.govParty)Socialist  0.016679     0.054161   0.308  0.76210   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3611 on 967 degrees of freedom
Multiple R-squared(full model): 0.05973   Adjusted R-squared: 0.01403 
Multiple R-squared(proj model): 0.00893   Adjusted R-squared: -0.03924 
F-statistic(full model, *iid*):1.307 on 47 and 967 DF, p-value: 0.08344 
F-statistic(proj model): 6.378 on 7 and 16 DF, p-value: 0.001071 



> stargazer(fit.fe0, fit.fe1, fit.fe2, fit.fe3, fit.fe4, fit.fe5, fit.fe6
+   , title = "Fixed Effect Models"
+   , omit = "Constant"
+   , covariate.labels = c("Years in Government"
+     , "Coalition"
+     , "Post Election"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist")
+   , digits = 2, omit.stat = c("ser", "adj.rsq", "rsq")
+   , dep.var.caption = "", dep.var.labels = ""
+   , column.labels = c("Naive Model", "Region FE", "Time FE", "Region + Time FE", "Region + Time FE", "Region + Time FE", "Region + Time FE")
+   , add.lines = list(c("Region FE", "no", "yes", "no", "yes", "yes", "yes", "yes"),
+     c("Year FE", "no", "no", "yes", "yes", "yes", "yes", "yes"))
+   , type = "text")

Fixed Effect Models
==========================================================================================================================
                                                                                                                          
                         Naive Model Region FE Time FE Region + Time FE Region + Time FE Region + Time FE Region + Time FE
                             (1)        (2)      (3)         (4)              (5)              (6)              (7)       
--------------------------------------------------------------------------------------------------------------------------
Years in Government       -0.01***    -0.01**  -0.002       -0.002           -0.002         -0.005***         -0.01***    
                           (0.002)    (0.002)  (0.002)     (0.003)          (0.003)          (0.002)          (0.002)     
                                                                                                                          
Coalition                   -0.02      -0.04    0.03                         0.003                             -0.01      
                           (0.03)     (0.04)   (0.03)                        (0.03)                            (0.03)     
                                                                                                                          
Post Election              0.05**      0.04     0.03                          0.03                              0.01      
                           (0.02)     (0.03)   (0.03)                        (0.03)                            (0.03)     
                                                                                                                          
Public Sector Vote Share    0.02       -0.18    0.21*                        -0.04                              0.04      
                           (0.12)     (0.33)   (0.12)                        (0.33)                            (0.28)     
                                                                                                                          
Return on Assetst-1        -0.004     -0.005   -0.05**                       -0.05*                            -0.05      
                           (0.02)     (0.03)   (0.02)                        (0.02)                            (0.04)     
                                                                                                                          
Party: Other                -0.02      -0.05    -0.03                        -0.07                             -0.09*     
                           (0.03)     (0.04)   (0.03)                        (0.06)                            (0.05)     
                                                                                                                          
Party: Socialist            0.03       0.08    -0.003                         0.03                              0.02      
                           (0.03)     (0.06)   (0.03)                        (0.06)                            (0.05)     
                                                                                                                          
--------------------------------------------------------------------------------------------------------------------------
Region FE                    no         yes      no          yes              yes              yes              yes       
Year FE                      no         no       yes         yes              yes              yes              yes       
Observations                1,259      1,259    1,259       1,259            1,259            1,015            1,015      
==========================================================================================================================
Note:                                                                                          *p<0.1; **p<0.05; ***p<0.01

> ### number of observations in models 4 and 6 differ slightly from the submitted manuscript
> ### substantive conclusions are not affected
> ### this was discussed and accepted by the editor
> 
> # **Table A10**: Explaining mergers in Spanish savings banks ------------------
> 
> ## Column 1
> fit.feMerger <- felm(bank.mergerSubject ~ region.yrsInGov +
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) | year + region.id | 0 | region.id
+   , data = cajas.boards, subset = bank.chair == 1, exactDOF=TRUE)

> summary(fit.feMerger)

Call:
   felm(formula = bank.mergerSubject ~ region.yrsInGov + region.coal +      region.postElec + bank.polVote + bank.roaLag1 + as.factor(region.govParty) |      year + region.id | 0 | region.id, data = cajas.boards, exactDOF = TRUE,      subset = bank.chair == 1) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.92382 -0.19967  0.01589  0.11282  0.82485 

Coefficients:
                                     Estimate Cluster s.e. t value Pr(>|t|)  
region.yrsInGov                     -0.004072     0.005054  -0.806   0.4322  
region.coal                          0.001444     0.053032   0.027   0.9786  
region.postElec                      0.007299     0.020446   0.357   0.7258  
bank.polVote                        -0.533446     0.452029  -1.180   0.2552  
bank.roaLag1                        -0.092684     0.034589  -2.680   0.0164 *
as.factor(region.govParty)Others     0.022683     0.097272   0.233   0.8186  
as.factor(region.govParty)Socialist  0.121430     0.072620   1.672   0.1139  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2989 on 1211 degrees of freedom
Multiple R-squared(full model): 0.4501   Adjusted R-squared: 0.4288 
Multiple R-squared(proj model): 0.03357   Adjusted R-squared: -0.003934 
F-statistic(full model, *iid*):21.09 on 47 and 1211 DF, p-value: < 2.2e-16 
F-statistic(proj model): 1.882 on 7 and 16 DF, p-value: 0.1397 



> stargazer(fit.feMerger
+   , title = "Explaining mergers in Spanish savings banks", label="T:fe.merger"
+   , omit = "Constant"
+   , covariate.labels = c("Years in Government"
+     , "Coalition"
+     , "Post Election"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist")
+   , digits = 2, omit.stat = c("ser", "adj.rsq", "rsq")
+   , dep.var.caption = "", dep.var.labels = "Bank Merger"
+   , type = "text")

Explaining mergers in Spanish savings banks
====================================================
                                 Bank Merger        
----------------------------------------------------
Years in Government                -0.004           
                                   (0.01)           
                                                    
Coalition                           0.001           
                                   (0.05)           
                                                    
Post Election                       0.01            
                                   (0.02)           
                                                    
Public Sector Vote Share            -0.53           
                                   (0.45)           
                                                    
Return on Assetst-1                -0.09**          
                                   (0.03)           
                                                    
Party: Other                        0.02            
                                   (0.10)           
                                                    
Party: Socialist                    0.12            
                                   (0.07)           
                                                    
----------------------------------------------------
Observations                        1,259           
====================================================
Note:                    *p<0.1; **p<0.05; ***p<0.01

> # **Table A11**: Robustness to different merger codings -----------------------
> 
> ## Column 1: all merged as right-censored
> fit.allCnsr <- coxph(Surv(bank.yrsInOffice, bank.mergerAllCnsr) ~ region.yrsInGov + 
+     region.coal + 
+     region.postElec + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1)

> summary(fit.allCnsr)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.mergerAllCnsr) ~ 
    region.yrsInGov + region.coal + region.postElec + bank.polVote + 
        bank.roaLag1 + as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
        frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1, method = "efron")

  n= 1259, number of events= 208 

                          coef     se(coef) se2     Chisq DF    p      
region.yrsInGov           -0.05339 0.01618  0.01537 10.89  1.00 9.6e-04
region.coal               -0.21119 0.18691  0.18127  1.28  1.00 2.6e-01
region.postElec            0.31407 0.14846  0.14795  4.48  1.00 3.4e-02
bank.polVote               0.05285 0.91472  0.78853  0.00  1.00 9.5e-01
bank.roaLag1               0.01730 0.15350  0.14720  0.01  1.00 9.1e-01
as.factor(region.govParty -0.14784 0.32566  0.29515  0.21  1.00 6.5e-01
as.factor(region.govParty  0.56433 0.29699  0.27614  3.61  1.00 5.7e-02
frailty(bank.id, distribu                           49.26 29.26 1.2e-02
as.factor(region.govParty -7.05660 1.07247  1.07238 43.29  1.00 4.7e-11
as.factor(region.govParty -7.05926 1.07204  1.07193 43.36  1.00 4.6e-11
as.factor(region.govParty -7.14324 1.07216  1.07206 44.39  1.00 2.7e-11

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           0.9480066     1.0548 9.184e-01  0.978547
region.coal               0.8096165     1.2352 5.613e-01  1.167826
region.postElec           1.3689902     0.7305 1.023e+00  1.831335
bank.polVote              1.0542744     0.9485 1.755e-01  6.332391
bank.roaLag1              1.0174521     0.9828 7.531e-01  1.374583
as.factor(region.govParty 0.8625688     1.1593 4.556e-01  1.633042
as.factor(region.govParty 1.7582736     0.5687 9.824e-01  3.146886
as.factor(region.govParty 0.0008617  1160.4977 1.053e-04  0.007051
as.factor(region.govParty 0.0008594  1163.5883 1.051e-04  0.007026
as.factor(region.govParty 0.0007902  1265.5204 9.663e-05  0.006462

Iterations: 7 outer, 140 Newton-Raphson
     Variance of random effect= 0.2721516 
Degrees of freedom for terms=  0.9  0.9  1.0  0.7  0.9  1.7 29.3  2.8 
Concordance= 0.942  (se = 0.942 )
Likelihood ratio test= 707.2  on 38.25 df,   p=<2e-16


> ## Column 2: all merged as 'event'
> fit.allEvnt <- coxph(Surv(bank.yrsInOffice, bank.mergerAllEvent) ~ region.yrsInGov + 
+     region.coal + 
+     region.postElec + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1)

> summary(fit.allEvnt)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.mergerAllEvent) ~ 
    region.yrsInGov + region.coal + region.postElec + bank.polVote + 
        bank.roaLag1 + as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
        frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1, method = "efron")

  n= 1259, number of events= 248 

                          coef     se(coef) se2     Chisq DF    p      
region.yrsInGov           -0.05922 0.01552  0.01463 14.55  1.00 1.4e-04
region.coal               -0.19413 0.17511  0.16911  1.23  1.00 2.7e-01
region.postElec            0.17037 0.13517  0.13458  1.59  1.00 2.1e-01
bank.polVote              -0.60477 0.89591  0.74353  0.46  1.00 5.0e-01
bank.roaLag1               0.11826 0.13858  0.13134  0.73  1.00 3.9e-01
as.factor(region.govParty -0.06865 0.31353  0.27658  0.05  1.00 8.3e-01
as.factor(region.govParty  0.94073 0.28196  0.25805 11.13  1.00 8.5e-04
frailty(bank.id, distribu                           74.05 38.95 5.9e-04
as.factor(region.govParty -7.18158 0.83414  0.83394 74.13  1.00 7.3e-18
as.factor(region.govParty -7.23072 0.83386  0.83365 75.19  1.00 4.3e-18
as.factor(region.govParty -7.32175 0.83412  0.83395 77.05  1.00 1.7e-18

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           0.9425021     1.0610 0.9142605  0.971616
region.coal               0.8235541     1.2142 0.5843038  1.160768
region.postElec           1.1857440     0.8434 0.9097728  1.545429
bank.polVote              0.5462020     1.8308 0.0943526  3.161931
bank.roaLag1              1.1255327     0.8885 0.8578232  1.476789
as.factor(region.govParty 0.9336493     1.0711 0.5050158  1.726087
as.factor(region.govParty 2.5618627     0.3903 1.4741812  4.452058
as.factor(region.govParty 0.0007605  1314.9893 0.0001483  0.003900
as.factor(region.govParty 0.0007240  1381.2107 0.0001412  0.003711
as.factor(region.govParty 0.0006610  1512.8520 0.0001289  0.003390

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.3732767 
Degrees of freedom for terms=  0.9  0.9  1.0  0.7  0.9  1.6 38.9  2.8 
Concordance= 0.949  (se = 0.949 )
Likelihood ratio test= 868  on 47.77 df,   p=<2e-16


> ## Column 3: only banks never involved in merger
> fit.noMerger <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
+     region.coal + 
+     region.postElec + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & bank.mergerSubject == 0 & bank.mergerResult == 0)

> summary(fit.noMerger)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1 & bank.mergerSubject == 0 & bank.mergerResult == 
        0, method = "efron")

  n= 769, number of events= 111 

                          coef     se(coef) se2     Chisq DF    p      
region.yrsInGov           -0.05682 0.02199  0.02125  6.68  1.00 0.00980
region.coal               -0.21918 0.25690  0.24703  0.73  1.00 0.39000
region.postElec            0.67619 0.21651  0.21603  9.75  1.00 0.00180
bank.polVote               1.11507 1.35768  1.10989  0.67  1.00 0.41000
bank.roaLag1               0.25232 0.24087  0.23087  1.10  1.00 0.29000
as.factor(region.govParty -0.81356 0.50494  0.45678  2.60  1.00 0.11000
as.factor(region.govParty  0.32541 0.45441  0.42423  0.51  1.00 0.47000
frailty(bank.id, distribu                           39.13 17.56 0.00220
as.factor(region.govParty -5.39021 1.47339  1.47334 13.38  1.00 0.00025
as.factor(region.govParty -5.32612 1.47287  1.47278 13.08  1.00 0.00030
as.factor(region.govParty -5.43934 1.47293  1.47287 13.64  1.00 0.00022

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov            0.944763     1.0585 0.9049146   0.98637
region.coal                0.803178     1.2451 0.4854470   1.32887
region.postElec            1.966371     0.5086 1.2863913   3.00579
bank.polVote               3.049773     0.3279 0.2131139  43.64386
bank.roaLag1               1.287013     0.7770 0.8027044   2.06353
as.factor(region.govParty  0.443278     2.2559 0.1647683   1.19256
as.factor(region.govParty  1.384597     0.7222 0.5682373   3.37378
as.factor(region.govParty  0.004561   219.2494 0.0002540   0.08189
as.factor(region.govParty  0.004863   205.6393 0.0002711   0.08722
as.factor(region.govParty  0.004342   230.2908 0.0002421   0.07789

Iterations: 5 outer, 100 Newton-Raphson
     Variance of random effect= 0.4816517 
Degrees of freedom for terms=  0.9  0.9  1.0  0.7  0.9  1.7 17.6  2.8 
Concordance= 0.954  (se = 0.954 )
Likelihood ratio test= 427.8  on 26.45 df,   p=<2e-16


> screenreg(list(fit.allCnsr, fit.allEvnt, fit.noMerger),
+   caption = "Robustness of findings to different codings of bank mergers"
+   , caption.above = T,
+   custom.coef.names = c("Years in Government"
+     , "Coalition"
+     , "Post Election"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist"),
+   include.rsquared=F, include.maxrs=F, include.missings=F, include.zph=F,
+   custom.model.names = c("All right-censored", "All event", "Banks never involved in merger"),
+   reorder.coef = c(1, 3, 2, 4:7),
+   stars = c(.01,.05,.1),
+   omit.coef = c("bank.yrsInOffice"))

=========================================================================================
                          All right-censored  All event    Banks never involved in merger
-----------------------------------------------------------------------------------------
Years in Government         -0.05 ***           -0.06 ***   -0.06 ***                    
                            (0.02)              (0.02)      (0.02)                       
Post Election                0.31 **             0.17        0.68 ***                    
                            (0.15)              (0.14)      (0.22)                       
Coalition                   -0.21               -0.19       -0.22                        
                            (0.19)              (0.18)      (0.26)                       
Public Sector Vote Share     0.05               -0.60        1.12                        
                            (0.91)              (0.90)      (1.36)                       
Return on Assets$_{t-1}$     0.02                0.12        0.25                        
                            (0.15)              (0.14)      (0.24)                       
Party: Other                -0.15               -0.07       -0.81                        
                            (0.33)              (0.31)      (0.50)                       
Party: Socialist             0.56 *              0.94 ***    0.33                        
                            (0.30)              (0.28)      (0.45)                       
-----------------------------------------------------------------------------------------
AIC                       1988.57             2365.86      881.16                        
Num. events                208                 248         111                           
Num. obs.                 1259                1259         769                           
=========================================================================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> # **Table A12**: restricted time window ---------------------------------------
> 
> ## Column 1
> fit.post1990 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
+     region.coal + 
+     region.postElec + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & year >= 1990)

> summary(fit.post1990)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1 & year >= 1990, method = "efron")

  n= 999, number of events= 165 

                          coef     se(coef) se2     Chisq  DF    p      
region.yrsInGov           -0.04473 0.01724  0.01625   6.73  1.00 9.5e-03
region.coal               -0.20390 0.21678  0.20932   0.88  1.00 3.5e-01
region.postElec            0.37458 0.16600  0.16543   5.09  1.00 2.4e-02
bank.polVote               0.97861 1.10473  0.92057   0.78  1.00 3.8e-01
bank.roaLag1              -0.04807 0.22216  0.21105   0.05  1.00 8.3e-01
as.factor(region.govParty -0.32887 0.40070  0.35710   0.67  1.00 4.1e-01
as.factor(region.govParty  0.57986 0.35787  0.33162   2.63  1.00 1.1e-01
frailty(bank.id, distribu                            48.42 26.01 4.9e-03
as.factor(region.govParty -6.07515 0.56593  0.56542 115.24  1.00 7.0e-27
as.factor(region.govParty -6.08286 0.56475  0.56418 116.01  1.00 4.7e-27
as.factor(region.govParty -6.17873 0.56556  0.56503 119.36  1.00 8.8e-28

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov            0.956258     1.0457 0.9244855  0.989122
region.coal                0.815542     1.2262 0.5332425  1.247291
region.postElec            1.454379     0.6876 1.0504530  2.013625
bank.polVote               2.660767     0.3758 0.3052524 23.192879
bank.roaLag1               0.953062     1.0492 0.6166161  1.473085
as.factor(region.govParty  0.719736     1.3894 0.3281698  1.578510
as.factor(region.govParty  1.785780     0.5600 0.8855460  3.601179
as.factor(region.govParty  0.002299   434.9154 0.0007584  0.006971
as.factor(region.govParty  0.002282   438.2829 0.0007543  0.006902
as.factor(region.govParty  0.002073   482.3802 0.0006842  0.006281

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.3530951 
Degrees of freedom for terms=  0.9  0.9  1.0  0.7  0.9  1.6 26.0  2.8 
Concordance= 0.958  (se = 0.958 )
Likelihood ratio test= 619.7  on 34.83 df,   p=<2e-16


> ## Column 2
> fit.pre2008 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
+     region.coal + 
+     region.postElec + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & year < 2008)

> summary(fit.pre2008)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1 & year < 2008, method = "efron")

  n= 1142, number of events= 221 

                          coef     se(coef) se2     Chisq DF    p      
region.yrsInGov           -0.05421 0.01727  0.01636  9.85  1.00 1.7e-03
region.coal               -0.16212 0.18862  0.18211  0.74  1.00 3.9e-01
region.postElec            0.26107 0.14210  0.14159  3.38  1.00 6.6e-02
bank.polVote              -0.09445 0.88919  0.76031  0.01  1.00 9.2e-01
bank.roaLag1              -0.04933 0.14749  0.14052  0.11  1.00 7.4e-01
as.factor(region.govParty  0.08087 0.32606  0.29391  0.06  1.00 8.0e-01
as.factor(region.govParty  0.84339 0.30007  0.27636  7.90  1.00 4.9e-03
frailty(bank.id, distribu                           56.41 32.97 6.7e-03
as.factor(region.govParty -7.21956 1.68339  1.68333 18.39  1.00 1.8e-05
as.factor(region.govParty -7.29131 1.68323  1.68317 18.76  1.00 1.5e-05
as.factor(region.govParty -7.37210 1.68332  1.68325 19.18  1.00 1.2e-05

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           0.9472309     1.0557 9.157e-01   0.97984
region.coal               0.8503413     1.1760 5.875e-01   1.23070
region.postElec           1.2983141     0.7702 9.827e-01   1.71528
bank.polVote              0.9098729     1.0991 1.593e-01   5.19830
bank.roaLag1              0.9518652     1.0506 7.129e-01   1.27092
as.factor(region.govParty 1.0842344     0.9223 5.722e-01   2.05431
as.factor(region.govParty 2.3242404     0.4302 1.291e+00   4.18502
as.factor(region.govParty 0.0007321  1365.8898 2.702e-05   0.01984
as.factor(region.govParty 0.0006814  1467.4969 2.516e-05   0.01846
as.factor(region.govParty 0.0006285  1590.9733 2.320e-05   0.01703

Iterations: 8 outer, 160 Newton-Raphson
     Variance of random effect= 0.3051034 
Degrees of freedom for terms=  0.9  0.9  1.0  0.7  0.9  1.7 33.0  2.8 
Concordance= 0.943  (se = 0.943 )
Likelihood ratio test= 751.2  on 41.92 df,   p=<2e-16


> ## Column 3
> fit.post1990pre2008 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
+     region.coal + 
+     region.postElec + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & year >= 1990 & year < 2008)

> summary(fit.post1990pre2008)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1 & year >= 1990 & year < 2008, method = "efron")

  n= 882, number of events= 152 

                          coef     se(coef) se2     Chisq DF    p      
region.yrsInGov           -0.04779 0.01932  0.01829  6.12  1.00 1.3e-02
region.coal               -0.23515 0.23425  0.22483  1.01  1.00 3.2e-01
region.postElec            0.38040 0.17182  0.17124  4.90  1.00 2.7e-02
bank.polVote               0.68662 1.11180  0.93942  0.38  1.00 5.4e-01
bank.roaLag1              -0.15016 0.23456  0.22166  0.41  1.00 5.2e-01
as.factor(region.govParty -0.10150 0.41856  0.37595  0.06  1.00 8.1e-01
as.factor(region.govParty  0.85579 0.37769  0.34867  5.13  1.00 2.3e-02
frailty(bank.id, distribu                           44.93 24.84 8.0e-03
as.factor(region.govParty -6.22228 0.74113  0.74077 70.49  1.00 4.6e-17
as.factor(region.govParty -6.28270 0.74038  0.74000 72.01  1.00 2.1e-17
as.factor(region.govParty -6.38759 0.74119  0.74076 74.27  1.00 6.8e-18

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov            0.953329     1.0490 0.9179076  0.990118
region.coal                0.790454     1.2651 0.4994371  1.251044
region.postElec            1.462872     0.6836 1.0445989  2.048629
bank.polVote               1.986992     0.5033 0.2248178 17.561494
bank.roaLag1               0.860569     1.1620 0.5434124  1.362831
as.factor(region.govParty  0.903482     1.1068 0.3977775  2.052100
as.factor(region.govParty  2.353230     0.4249 1.1224615  4.933523
as.factor(region.govParty  0.001985   503.8524 0.0004643  0.008483
as.factor(region.govParty  0.001868   535.2314 0.0004378  0.007974
as.factor(region.govParty  0.001682   594.4206 0.0003936  0.007191

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.3493677 
Degrees of freedom for terms=  0.9  0.9  1.0  0.7  0.9  1.7 24.8  2.8 
Concordance= 0.957  (se = 0.957 )
Likelihood ratio test= 572.1  on 33.69 df,   p=<2e-16


> screenreg(list(fit.post1990, fit.pre2008, fit.post1990pre2008),
+   caption = "Cox PH model with subsample for observations between 1990–2007", 
+   caption.above = T,
+   custom.coef.names = c("Years in Government"
+     , "Coalition"
+     , "Post Election"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist"),
+   include.rsquared=F, include.maxrs=F, include.missings=F, include.zph=F,
+   custom.model.names = c("excl. pre-1990", "excl. post-2007", "1990-2007"),
+   reorder.coef = c(1, 3, 2, 4:7),
+   stars = c(.01,.05,.1),
+   omit.coef = c("bank.yrsInOffice"))

=====================================================================
                          excl. pre-1990  excl. post-2007  1990-2007 
---------------------------------------------------------------------
Years in Government         -0.04 ***       -0.05 ***        -0.05 **
                            (0.02)          (0.02)           (0.02)  
Post Election                0.37 **         0.26 *           0.38 **
                            (0.17)          (0.14)           (0.17)  
Coalition                   -0.20           -0.16            -0.24   
                            (0.22)          (0.19)           (0.23)  
Public Sector Vote Share     0.98           -0.09             0.69   
                            (1.10)          (0.89)           (1.11)  
Return on Assets$_{t-1}$    -0.05           -0.05            -0.15   
                            (0.22)          (0.15)           (0.23)  
Party: Other                -0.33            0.08            -0.10   
                            (0.40)          (0.33)           (0.42)  
Party: Socialist             0.58            0.84 ***         0.86 **
                            (0.36)          (0.30)           (0.38)  
---------------------------------------------------------------------
AIC                       1431.99         2104.05          1307.10   
Num. events                165             221              152      
Num. obs.                  999            1142              882      
=====================================================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> ### **Table A13**: hazard over cycle ------------------------------------------
> 
> ## Column 1
> fit.cycle <- coxph(Surv(bank.yrsInOffice, bank.event) ~ factor(region.elecCycle) +
+     region.yrsInGov +
+     region.coal + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1)

> summary(fit.cycle)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ factor(region.elecCycle) + 
    region.yrsInGov + region.coal + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1, method = "efron")

  n= 1259, number of events= 234 

                          coef     se(coef) se2     Chisq  DF    p      
factor(region.elecCycle)2 -0.12755 0.17551  0.17514   0.53  1.00 4.7e-01
factor(region.elecCycle)3 -0.43542 0.19421  0.19357   5.03  1.00 2.5e-02
factor(region.elecCycle)4 -0.32423 0.19956  0.19875   2.64  1.00 1.0e-01
region.yrsInGov           -0.06209 0.01609  0.01517  14.89  1.00 1.1e-04
region.coal               -0.21410 0.18014  0.17393   1.41  1.00 2.3e-01
bank.polVote              -0.01328 0.91770  0.76582   0.00  1.00 9.9e-01
bank.roaLag1               0.06543 0.14509  0.13767   0.20  1.00 6.5e-01
as.factor(region.govParty -0.12400 0.32517  0.28878   0.15  1.00 7.0e-01
as.factor(region.govParty  0.89095 0.29127  0.26732   9.36  1.00 2.2e-03
frailty(bank.id, distribu                            72.37 37.81 6.0e-04
as.factor(region.govParty -7.64212 0.52493  0.52456 211.94  1.00 5.2e-48
as.factor(region.govParty -7.66966 0.52434  0.52393 213.96  1.00 1.9e-48
as.factor(region.govParty -7.77264 0.52485  0.52450 219.31  1.00 1.3e-49

                          exp(coef) exp(-coef) lower .95 upper .95
factor(region.elecCycle)2 0.8802503     1.1360 0.6240438  1.241645
factor(region.elecCycle)3 0.6469940     1.5456 0.4421652  0.946708
factor(region.elecCycle)4 0.7230815     1.3830 0.4890163  1.069181
region.yrsInGov           0.9397965     1.0641 0.9106244  0.969903
region.coal               0.8072668     1.2387 0.5671257  1.149092
bank.polVote              0.9868114     1.0134 0.1633390  5.961814
bank.roaLag1              1.0676138     0.9367 0.8033627  1.418785
as.factor(region.govParty 0.8833806     1.1320 0.4670450  1.670848
as.factor(region.govParty 2.4374464     0.4103 1.3772179  4.313874
as.factor(region.govParty 0.0004798  2084.1678 0.0001715  0.001342
as.factor(region.govParty 0.0004668  2142.3464 0.0001670  0.001304
as.factor(region.govParty 0.0004211  2374.7313 0.0001505  0.001178

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.3791819 
Degrees of freedom for terms=  3.0  0.9  0.9  0.7  0.9  1.6 37.8  2.8 
Concordance= 0.945  (se = 0.945 )
Likelihood ratio test= 810.6  on 48.64 df,   p=<2e-16


> screenreg(list(fit.cycle),
+   caption = "Alternative specification of election variable", caption.above = T,
+   custom.coef.names = c("Election$_{t+1}$"
+     , "Election$_{t+2}$"
+     , "Election$_{t+3}$"
+     , "Years in Government"
+     , "Coalition"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist"),
+   include.rsquared=F, include.maxrs=F, include.missings=F, include.zph=F,
+   stars = c(.01,.05,.1),
+   omit.coef = c("bank.yrsInOffice"))

=====================================
                          Model 1    
-------------------------------------
Election$_{t+1}$            -0.13    
                            (0.18)   
Election$_{t+2}$            -0.44 ** 
                            (0.19)   
Election$_{t+3}$            -0.32    
                            (0.20)   
Years in Government         -0.06 ***
                            (0.02)   
Coalition                   -0.21    
                            (0.18)   
Public Sector Vote Share    -0.01    
                            (0.92)   
Return on Assets$_{t-1}$     0.07    
                            (0.15)   
Party: Other                -0.12    
                            (0.33)   
Party: Socialist             0.89 ***
                            (0.29)   
-------------------------------------
AIC                       2241.11    
Num. events                234       
Num. obs.                 1259       
=====================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> # **Table A14**: interaction / pol vote share ---------------------------------
> 
> ## Column 1
> fit.interact1 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov +
+     region.postElec * bank.polVote + 
+     region.coal +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1)

> summary(fit.interact1)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.postElec * bank.polVote + region.coal + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1, method = "efron")

  n= 1259, number of events= 234 

                          coef     se(coef) se2     Chisq DF    p      
region.yrsInGov           -0.05761 0.01575  0.01487 13.38  1.00 2.5e-04
region.postElec           -0.50880 0.59501  0.59329  0.73  1.00 3.9e-01
bank.polVote              -0.95314 1.15432  1.03998  0.68  1.00 4.1e-01
region.coal               -0.20916 0.17937  0.17333  1.36  1.00 2.4e-01
bank.roaLag1               0.03922 0.14508  0.13803  0.07  1.00 7.9e-01
as.factor(region.govParty -0.09394 0.32042  0.28479  0.09  1.00 7.7e-01
as.factor(region.govParty  0.80653 0.28892  0.26565  7.79  1.00 5.2e-03
frailty(bank.id, distribu                           67.59 36.54 1.3e-03
region.postElec:bank.polV  1.69841 1.24705  1.24381  1.85  1.00 1.7e-01
as.factor(region.govParty -7.19740 0.85605  0.85585 70.69  1.00 4.2e-17
as.factor(region.govParty -7.22566 0.85568  0.85547 71.31  1.00 3.1e-17
as.factor(region.govParty -7.31802 0.85593  0.85576 73.10  1.00 1.2e-17

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           0.9440212     1.0593 0.9153277  0.973614
region.postElec           0.6012188     1.6633 0.1873091  1.929773
bank.polVote              0.3855270     2.5939 0.0401323  3.703523
region.coal               0.8112687     1.2326 0.5707977  1.153048
bank.roaLag1              1.0400045     0.9615 0.7826123  1.382050
as.factor(region.govParty 0.9103335     1.0985 0.4857964  1.705873
as.factor(region.govParty 2.2401311     0.4464 1.2715884  3.946393
region.postElec:bank.polV 5.4652378     0.1830 0.4743687 62.965419
as.factor(region.govParty 0.0007485  1335.9488 0.0001398  0.004008
as.factor(region.govParty 0.0007277  1374.2399 0.0001360  0.003893
as.factor(region.govParty 0.0006635  1507.2155 0.0001240  0.003551

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.3539397 
Degrees of freedom for terms=  0.9  1.0  0.8  0.9  0.9  1.6 36.5  1.0  2.8 
Concordance= 0.947  (se = 0.947 )
Likelihood ratio test= 816.2  on 46.51 df,   p=<2e-16


> ## Column 2
> fit.interact2 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.postElec * bank.polVote + 
+     region.coal +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & region.govChange == 1)

> summary(fit.interact2)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.postElec * 
    bank.polVote + region.coal + bank.roaLag1 + as.factor(region.govParty) + 
    as.factor(region.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cajas.boards, subset = bank.chair == 
    1 & region.govChange == 1, method = "efron")

  n= 445, number of events= 94 

                          coef     se(coef) se2    Chisq DF    p      
region.postElec           -1.31189 1.0431   1.0345  1.58  1.00 2.1e-01
bank.polVote              -1.19054 1.7420   1.5943  0.47  1.00 4.9e-01
region.coal               -0.02624 0.2514   0.2437  0.01  1.00 9.2e-01
bank.roaLag1              -0.22552 0.2527   0.2410  0.80  1.00 3.7e-01
as.factor(region.govParty  0.74111 0.4683   0.4346  2.50  1.00 1.1e-01
as.factor(region.govParty  0.38857 0.5804   0.5562  0.45  1.00 5.0e-01
frailty(bank.id, distribu                          25.68 16.32 6.5e-02
region.postElec:bank.polV  3.25733 2.0958   2.0802  2.42  1.00 1.2e-01
as.factor(region.govParty -7.76779 1.5799   1.5795 24.17  1.00 8.8e-07
as.factor(region.govParty -8.05158 1.5786   1.5782 26.01  1.00 3.4e-07
as.factor(region.govParty -8.09834 1.5791   1.5786 26.30  1.00 2.9e-07

                          exp(coef) exp(-coef) lower .95 upper .95
region.postElec           2.693e-01  3.713e+00 3.486e-02 2.080e+00
bank.polVote              3.041e-01  3.289e+00 1.000e-02 9.242e+00
region.coal               9.741e-01  1.027e+00 5.951e-01 1.595e+00
bank.roaLag1              7.981e-01  1.253e+00 4.863e-01 1.310e+00
as.factor(region.govParty 2.098e+00  4.766e-01 8.380e-01 5.254e+00
as.factor(region.govParty 1.475e+00  6.780e-01 4.728e-01 4.601e+00
region.postElec:bank.polV 2.598e+01  3.849e-02 4.273e-01 1.580e+03
as.factor(region.govParty 4.231e-04  2.363e+03 1.913e-05 9.360e-03
as.factor(region.govParty 3.186e-04  3.139e+03 1.444e-05 7.030e-03
as.factor(region.govParty 3.040e-04  3.289e+03 1.377e-05 6.715e-03

Iterations: 7 outer, 140 Newton-Raphson
     Variance of random effect= 0.3396449 
Degrees of freedom for terms=  1.0  0.8  0.9  0.9  1.8 16.3  1.0  2.7 
Concordance= 0.937  (se = 0.937 )
Likelihood ratio test= 315  on 25.46 df,   p=<2e-16


> ## Column 3
> fit.interact3 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.postElec * bank.polVote + 
+     region.coal +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & region.govChange == 0)

> summary(fit.interact3)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.postElec * 
    bank.polVote + region.coal + bank.roaLag1 + as.factor(region.govParty) + 
    as.factor(region.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cajas.boards, subset = bank.chair == 
    1 & region.govChange == 0, method = "efron")

  n= 814, number of events= 140 

                          coef     se(coef) se2    Chisq DF    p      
region.postElec            0.03312 0.7508   0.7489  0.00  1.00 9.6e-01
bank.polVote              -1.86618 1.5377   1.4440  1.47  1.00 2.2e-01
region.coal                0.01374 0.2976   0.2796  0.00  1.00 9.6e-01
bank.roaLag1               0.08352 0.1891   0.1796  0.20  1.00 6.6e-01
as.factor(region.govParty -0.63724 0.5079   0.4501  1.57  1.00 2.1e-01
as.factor(region.govParty  1.12553 0.4131   0.3677  7.42  1.00 6.4e-03
frailty(bank.id, distribu                          33.25 21.92 5.7e-02
region.postElec:bank.polV  0.85931 1.6201   1.6150  0.28  1.00 6.0e-01
as.factor(region.govParty -6.46682 1.4894   1.4893 18.85  1.00 1.4e-05
as.factor(region.govParty -6.43126 1.4891   1.4890 18.65  1.00 1.6e-05
as.factor(region.govParty -6.54939 1.4891   1.4890 19.35  1.00 1.1e-05

                          exp(coef) exp(-coef) lower .95 upper .95
region.postElec            1.033675     0.9674 2.373e-01   4.50304
bank.polVote               0.154713     6.4636 7.597e-03   3.15064
region.coal                1.013837     0.9864 5.657e-01   1.81687
bank.roaLag1               1.087109     0.9199 7.504e-01   1.57491
as.factor(region.govParty  0.528751     1.8912 1.954e-01   1.43070
as.factor(region.govParty  3.081855     0.3245 1.371e+00   6.92584
region.postElec:bank.polV  2.361528     0.4235 9.866e-02  56.52521
as.factor(region.govParty  0.001554   643.4367 8.389e-05   0.02879
as.factor(region.govParty  0.001610   620.9532 8.697e-05   0.02982
as.factor(region.govParty  0.001431   698.8149 7.729e-05   0.02649

Iterations: 7 outer, 140 Newton-Raphson
     Variance of random effect= 0.2794523 
Degrees of freedom for terms=  1.0  0.9  0.9  0.9  1.6 21.9  1.0  2.9 
Concordance= 0.954  (se = 0.954 )
Likelihood ratio test= 504.6  on 31.05 df,   p=<2e-16


> screenreg(list(fit.interact1, fit.interact2, fit.interact3),
+   caption = "Cox PH Model exploring effect heterogeneity for public sector vote share", 
+   caption.above = T,
+   custom.coef.names = c("Years in Government"
+     , "Post Election"
+     , "Public Sector Vote Share"
+     , "Coalition"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist", "Post Election * Public Sector Vote Share"),
+   include.rsquared=F, include.maxrs=F, include.missings=F, include.zph=F,
+   reorder.coef = c(1:3,8,4:7),
+   stars = c(.01,.05,.1),
+   omit.coef = c("bank.yrsInOffice"))

===========================================================================
                                          Model 1      Model 2  Model 3    
---------------------------------------------------------------------------
Years in Government                         -0.06 ***                      
                                            (0.02)                         
Post Election                               -0.51       -1.31      0.03    
                                            (0.60)      (1.04)    (0.75)   
Public Sector Vote Share                    -0.95       -1.19     -1.87    
                                            (1.15)      (1.74)    (1.54)   
Post Election * Public Sector Vote Share     1.70        3.26      0.86    
                                            (1.25)      (2.10)    (1.62)   
Coalition                                   -0.21       -0.03      0.01    
                                            (0.18)      (0.25)    (0.30)   
Return on Assets$_{t-1}$                     0.04       -0.23      0.08    
                                            (0.15)      (0.25)    (0.19)   
Party: Other                                -0.09        0.74     -0.64    
                                            (0.32)      (0.47)    (0.51)   
Party: Socialist                             0.81 ***    0.39      1.13 ***
                                            (0.29)      (0.58)    (0.41)   
---------------------------------------------------------------------------
AIC                                       2231.21      708.83   1225.57    
Num. events                                234          94       140       
Num. obs.                                 1259         445       814       
===========================================================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> # **Table A15**: effect by bank size ------------------------------------------
> 
> ## generate year-specific asset quartiles
> cajas.boards <- cajas.boards[cajas.boards$bank.chair == 1,] %>%
+   group_by(year) %>%
+   mutate(m.assets = mean(bank.assetsTot, na.rm = T)
+     , qrt.assets = ntile(bank.assetsTot, 4))

> ## Column 1 - quartile 1
> fit.qrt1 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & qrt.assets == 1)

> summary(fit.qrt1)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1 & qrt.assets == 1, method = "efron")

  n= 316, number of events= 57 
   (27 observations deleted due to missingness)

                          coef     se(coef) se2     Chisq DF   p      
region.yrsInGov           -0.02694 0.03039  0.02772  0.79 1.00 3.8e-01
region.coal               -0.01219 0.42269  0.41694  0.00 1.00 9.8e-01
region.postElec            0.68344 0.30590  0.30423  4.99 1.00 2.5e-02
bank.polVote               0.56042 1.87427  1.69621  0.09 1.00 7.6e-01
bank.roaLag1               0.50338 0.31160  0.29612  2.61 1.00 1.1e-01
as.factor(region.govParty -0.37556 0.95886  0.90067  0.15 1.00 7.0e-01
as.factor(region.govParty  0.24726 0.56206  0.53059  0.19 1.00 6.6e-01
frailty(bank.id, distribu                           12.97 8.76 1.5e-01
as.factor(region.govParty -6.12358 1.50597  1.50527 16.53 1.00 4.8e-05
as.factor(region.govParty -6.14201 1.50808  1.50726 16.59 1.00 4.6e-05
as.factor(region.govParty -6.05965 1.50174  1.50135 16.28 1.00 5.5e-05

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov            0.973422     1.0273 0.9171426   1.03316
region.coal                0.987886     1.0123 0.4314353   2.26203
region.postElec            1.980688     0.5049 1.0875102   3.60744
bank.polVote               1.751411     0.5710 0.0444636  68.98761
bank.roaLag1               1.654306     0.6045 0.8982207   3.04683
as.factor(region.govParty  0.686905     1.4558 0.1048858   4.49859
as.factor(region.govParty  1.280518     0.7809 0.4255553   3.85315
as.factor(region.govParty  0.002191   456.4975 0.0001145   0.04192
as.factor(region.govParty  0.002151   464.9851 0.0001119   0.04133
as.factor(region.govParty  0.002335   428.2265 0.0001230   0.04432

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.3457399 
Degrees of freedom for terms= 0.8 1.0 1.0 0.8 0.9 1.8 8.8 2.9 
Concordance= 0.959  (se = 0.959 )
Likelihood ratio test= 215.1  on 17.98 df,   p=<2e-16


> ## Column 2 - quartile 1
> fit.qrt2 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & qrt.assets == 2)

> summary(fit.qrt2)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1 & qrt.assets == 2, method = "efron")

  n= 314, number of events= 60 
   (27 observations deleted due to missingness)

                          coef      se(coef) se2     Chisq DF   p      
region.yrsInGov            -0.09814 0.03907  0.03787  6.31 1.00 1.2e-02
region.coal                -0.33729 0.34735  0.33234  0.94 1.00 3.3e-01
region.postElec             0.27429 0.28743  0.28549  0.91 1.00 3.4e-01
bank.polVote                3.41140 1.63332  1.44580  4.36 1.00 3.7e-02
bank.roaLag1                0.11826 0.24165  0.22642  0.24 1.00 6.2e-01
as.factor(region.govParty  -2.84350 0.91603  0.89476  9.64 1.00 1.9e-03
as.factor(region.govParty   0.10497 0.84242  0.82048  0.02 1.00 9.0e-01
frailty(bank.id, distribu                             7.39 5.83 2.7e-01
as.factor(region.govParty -10.27753 1.34516  1.34376 58.38 1.00 2.2e-14
as.factor(region.govParty  -9.71361 1.32491  1.32403 53.75 1.00 2.3e-13
as.factor(region.govParty -10.17910 1.33812  1.33722 57.87 1.00 2.8e-14

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           9.065e-01  1.103e+00 8.397e-01 9.787e-01
region.coal               7.137e-01  1.401e+00 3.613e-01 1.410e+00
region.postElec           1.316e+00  7.601e-01 7.490e-01 2.311e+00
bank.polVote              3.031e+01  3.299e-02 1.234e+00 7.445e+02
bank.roaLag1              1.126e+00  8.885e-01 7.009e-01 1.807e+00
as.factor(region.govParty 5.822e-02  1.718e+01 9.668e-03 3.506e-01
as.factor(region.govParty 1.111e+00  9.003e-01 2.131e-01 5.790e+00
as.factor(region.govParty 3.440e-05  2.907e+04 2.463e-06 4.803e-04
as.factor(region.govParty 6.045e-05  1.654e+04 4.505e-06 8.113e-04
as.factor(region.govParty 3.796e-05  2.635e+04 2.756e-06 5.227e-04

Iterations: 7 outer, 140 Newton-Raphson
     Variance of random effect= 0.1650123 
Degrees of freedom for terms= 0.9 0.9 1.0 0.8 0.9 1.9 5.8 3.0 
Concordance= 0.954  (se = 0.954 )
Likelihood ratio test= 203.6  on 15.19 df,   p=<2e-16


> ## Column 3 - quartile 3
> fit.qrt3 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & qrt.assets == 3)

> summary(fit.qrt3)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1 & qrt.assets == 3, method = "efron")

  n= 304, number of events= 62 
   (27 observations deleted due to missingness)

                          coef     se(coef) se2     Chisq DF   p      
region.yrsInGov           -0.05468 0.03218  0.03068  2.89 1.00 8.9e-02
region.coal               -0.18547 0.32813  0.31775  0.32 1.00 5.7e-01
region.postElec            0.19647 0.27927  0.27770  0.49 1.00 4.8e-01
bank.polVote              -0.23488 1.48742  1.36751  0.02 1.00 8.7e-01
bank.roaLag1              -0.33007 0.31444  0.30279  1.10 1.00 2.9e-01
as.factor(region.govParty -0.04837 0.59784  0.57021  0.01 1.00 9.4e-01
as.factor(region.govParty  1.15046 0.55963  0.53174  4.23 1.00 4.0e-02
frailty(bank.id, distribu                            7.43 6.15 3.0e-01
as.factor(region.govParty -7.83770 0.88346  0.88269 78.70 1.00 7.2e-19
as.factor(region.govParty -7.86132 0.88502  0.88419 78.90 1.00 6.5e-19
as.factor(region.govParty -8.09593 0.88777  0.88694 83.16 1.00 7.6e-20

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           0.9467899     1.0562 8.889e-01  1.008436
region.coal               0.8307158     1.2038 4.367e-01  1.580350
region.postElec           1.2170928     0.8216 7.041e-01  2.103947
bank.polVote              0.7906678     1.2648 4.284e-02 14.591240
bank.roaLag1              0.7188754     1.3911 3.882e-01  1.331395
as.factor(region.govParty 0.9527835     1.0496 2.952e-01  3.075232
as.factor(region.govParty 3.1596578     0.3165 1.055e+00  9.462293
as.factor(region.govParty 0.0003946  2534.3696 6.984e-05  0.002229
as.factor(region.govParty 0.0003854  2594.9481 6.801e-05  0.002184
as.factor(region.govParty 0.0003048  3281.0706 5.349e-05  0.001736

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.1671085 
Degrees of freedom for terms= 0.9 0.9 1.0 0.8 0.9 1.8 6.2 2.9 
Concordance= 0.942  (se = 0.942 )
Likelihood ratio test= 200.1  on 15.46 df,   p=<2e-16


> ## Column 4 - quartile 4
> fit.qrt4 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & qrt.assets == 4)

> summary(fit.qrt4)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.coal + region.postElec + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1 & qrt.assets == 4, method = "efron")

  n= 298, number of events= 55 
   (27 observations deleted due to missingness)

                          coef     se(coef) se2     Chisq DF   p      
region.yrsInGov           -0.07814 0.0377   0.03554  4.30 1.00 3.8e-02
region.coal               -0.08620 0.4118   0.39011  0.04 1.00 8.3e-01
region.postElec            0.25112 0.2990   0.29660  0.71 1.00 4.0e-01
bank.polVote              -2.42099 2.2575   1.82801  1.15 1.00 2.8e-01
bank.roaLag1              -0.67151 0.2781   0.25914  5.83 1.00 1.6e-02
as.factor(region.govParty  0.75851 0.6294   0.54235  1.45 1.00 2.3e-01
as.factor(region.govParty  0.87493 0.6842   0.62750  1.64 1.00 2.0e-01
frailty(bank.id, distribu                           16.99 9.52 6.1e-02
as.factor(region.govParty -7.61089 0.8761   0.87448 75.47 1.00 3.7e-18
as.factor(region.govParty -7.75619 0.8742   0.87286 78.72 1.00 7.2e-19
as.factor(region.govParty -7.90523 0.8801   0.87794 80.68 1.00 2.7e-19

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           0.9248379     1.0813 8.590e-01  0.995767
region.coal               0.9174127     1.0900 4.093e-01  2.056516
region.postElec           1.2854649     0.7779 7.154e-01  2.309783
bank.polVote              0.0888334    11.2570 1.064e-03  7.416101
bank.roaLag1              0.5109381     1.9572 2.962e-01  0.881305
as.factor(region.govParty 2.1351015     0.4684 6.218e-01  7.330925
as.factor(region.govParty 2.3986983     0.4169 6.275e-01  9.169147
as.factor(region.govParty 0.0004950  2020.0673 8.890e-05  0.002756
as.factor(region.govParty 0.0004281  2335.9890 7.716e-05  0.002375
as.factor(region.govParty 0.0003688  2711.4338 6.571e-05  0.002070

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.4541686 
Degrees of freedom for terms= 0.9 0.9 1.0 0.7 0.9 1.6 9.5 2.9 
Concordance= 0.953  (se = 0.953 )
Likelihood ratio test= 187.8  on 18.27 df,   p=<2e-16


> screenreg(list(fit.qrt1, fit.qrt2, fit.qrt3, fit.qrt4),
+   caption = "Cox PH Model, splitting sample by bank size", caption.above = T,
+   custom.coef.names = c("Years in Government"
+     , "Coalition"
+     , "Post Election"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist"),
+   custom.model.names = c("Quartile 1", "Quartile 2", "Quartile 3", "Quartile 4"),
+   include.rsquared=F, include.maxrs=F, include.missings=F, include.zph=F,
+   stars = c(.01,.05,.1),
+   omit.coef = c("bank.yrsInOffice"))

========================================================================
                          Quartile 1  Quartile 2  Quartile 3  Quartile 4
------------------------------------------------------------------------
Years in Government        -0.03       -0.10 **    -0.05 *     -0.08 ** 
                           (0.03)      (0.04)      (0.03)      (0.04)   
Coalition                  -0.01       -0.34       -0.19       -0.09    
                           (0.42)      (0.35)      (0.33)      (0.41)   
Post Election               0.68 **     0.27        0.20        0.25    
                           (0.31)      (0.29)      (0.28)      (0.30)   
Public Sector Vote Share    0.56        3.41 **    -0.23       -2.42    
                           (1.87)      (1.63)      (1.49)      (2.26)   
Return on Assets$_{t-1}$    0.50        0.12       -0.33       -0.67 ** 
                           (0.31)      (0.24)      (0.31)      (0.28)   
Party: Other               -0.38       -2.84 ***   -0.05        0.76    
                           (0.96)      (0.92)      (0.60)      (0.63)   
Party: Socialist            0.25        0.10        1.15 **     0.87    
                           (0.56)      (0.84)      (0.56)      (0.68)   
------------------------------------------------------------------------
AIC                       381.21      403.81      450.61      385.29    
Num. events                57          60          62          55       
Num. obs.                 316         314         304         298       
========================================================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> ### estimates and number of observations in model 2-3 differ slightly from the submitted manuscript
> ### substantive conclusions are not affected
> ### this was discussed and accepted by the editor
> 
> # **Table A16**: polynomials for "Years in Government" ------------------------
> 
> ## Column 1 - main specification from manuscript (Table 1, Model 3)
> fit.main3 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov +
+     region.postElec +
+     region.coal + 
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1)

> summary(fit.main3)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    region.postElec + region.coal + bank.polVote + bank.roaLag1 + 
    as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1, method = "efron")

  n= 1259, number of events= 234 

                          coef     se(coef) se2     Chisq DF    p      
region.yrsInGov           -0.05776 0.01575  0.01487 13.44  1.00 2.5e-04
region.postElec            0.28108 0.13964  0.13908  4.05  1.00 4.4e-02
region.coal               -0.20405 0.17897  0.17299  1.30  1.00 2.5e-01
bank.polVote              -0.03099 0.90536  0.75864  0.00  1.00 9.7e-01
bank.roaLag1               0.03954 0.14522  0.13819  0.07  1.00 7.9e-01
as.factor(region.govParty -0.08520 0.31997  0.28459  0.07  1.00 7.9e-01
as.factor(region.govParty  0.80198 0.28840  0.26518  7.73  1.00 5.4e-03
frailty(bank.id, distribu                           67.07 36.43 1.5e-03
as.factor(region.govParty -7.18812 0.86614  0.86595 68.87  1.00 1.0e-16
as.factor(region.govParty -7.21841 0.86577  0.86558 69.51  1.00 7.6e-17
as.factor(region.govParty -7.30733 0.86602  0.86585 71.20  1.00 3.2e-17

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           0.9438785     1.0595 0.9151807  0.973476
region.postElec           1.3245562     0.7550 1.0074191  1.741529
region.coal               0.8154216     1.2264 0.5741750  1.158031
bank.polVote              0.9694863     1.0315 0.1643974  5.717267
bank.roaLag1              1.0403301     0.9612 0.7826391  1.382868
as.factor(region.govParty 0.9183316     1.0889 0.4905013  1.719329
as.factor(region.govParty 2.2299466     0.4484 1.2670902  3.924474
as.factor(region.govParty 0.0007555  1323.6158 0.0001383  0.004126
as.factor(region.govParty 0.0007330  1364.3203 0.0001343  0.004000
as.factor(region.govParty 0.0006706  1491.1833 0.0001228  0.003661

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.3520437 
Degrees of freedom for terms=  0.9  1.0  0.9  0.7  0.9  1.6 36.4  2.8 
Concordance= 0.946  (se = 0.946 )
Likelihood ratio test= 814.3  on 45.3 df,   p=<2e-16


> ## Column 2 - quadratic
> fit.quadratic <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + I(region.yrsInGov^2) +
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1)

> summary(fit.quadratic)  # loglik = 814.8
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    I(region.yrsInGov^2) + region.coal + region.postElec + bank.polVote + 
    bank.roaLag1 + as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice + 
    frailty(bank.id, distribution = "gaussian"), data = cajas.boards, 
    subset = bank.chair == 1, method = "efron")

  n= 1259, number of events= 234 

                          coef      se(coef) se2      Chisq DF    p      
region.yrsInGov           -0.021020 0.045988 0.045074  0.21  1.00 6.5e-01
I(region.yrsInGov^2)      -0.001685 0.001986 0.001956  0.72  1.00 4.0e-01
region.coal               -0.166521 0.184441 0.179010  0.82  1.00 3.7e-01
region.postElec            0.305790 0.142918 0.142201  4.58  1.00 3.2e-02
bank.polVote               0.031138 0.908155 0.760333  0.00  1.00 9.7e-01
bank.roaLag1               0.031152 0.145441 0.138235  0.05  1.00 8.3e-01
as.factor(region.govParty -0.108876 0.321514 0.285703  0.11  1.00 7.3e-01
as.factor(region.govParty  0.741535 0.296787 0.272659  6.24  1.00 1.2e-02
frailty(bank.id, distribu                             67.14 36.46 1.5e-03
as.factor(region.govParty -7.190923 0.864903 0.864712 69.12  1.00 9.2e-17
as.factor(region.govParty -7.215354 0.864432 0.864241 69.67  1.00 7.0e-17
as.factor(region.govParty -7.300893 0.864634 0.864466 71.30  1.00 3.1e-17

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           0.9791991     1.0212 0.8947992  1.071560
I(region.yrsInGov^2)      0.9983162     1.0017 0.9944374  1.002210
region.coal               0.8466053     1.1812 0.5897728  1.215282
region.postElec           1.3576977     0.7365 1.0260096  1.796614
bank.polVote              1.0316283     0.9693 0.1739804  6.117107
bank.roaLag1              1.0316423     0.9693 0.7757634  1.371921
as.factor(region.govParty 0.8968414     1.1150 0.4775759  1.684181
as.factor(region.govParty 2.0991548     0.4764 1.1733274  3.755517
as.factor(region.govParty 0.0007534  1327.3272 0.0001383  0.004104
as.factor(region.govParty 0.0007352  1360.1547 0.0001351  0.004001
as.factor(region.govParty 0.0006749  1481.6224 0.0001240  0.003675

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.3528198 
Degrees of freedom for terms=  1.0  1.0  0.9  1.0  0.7  0.9  1.6 36.5  2.8 
Concordance= 0.946  (se = 0.946 )
Likelihood ratio test= 814.8  on 46.36 df,   p=<2e-16


> ## Column 3 - cubic
> fit.cubic <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + I(region.yrsInGov^2) + I(region.yrsInGov^3) +
+     region.coal + 
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1)

> summary(fit.cubic) # loglik = 815
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.yrsInGov + 
    I(region.yrsInGov^2) + I(region.yrsInGov^3) + region.coal + 
    region.postElec + bank.polVote + bank.roaLag1 + as.factor(region.govParty) + 
    as.factor(region.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cajas.boards, subset = bank.chair == 
    1, method = "efron")

  n= 1259, number of events= 234 

                          coef       se(coef)  se2       Chisq DF    p      
region.yrsInGov            0.0155739 0.0989398 0.0976734  0.02  1.00 8.7e-01
I(region.yrsInGov^2)      -0.0058237 0.0097945 0.0096828  0.35  1.00 5.5e-01
I(region.yrsInGov^3)       0.0001202 0.0002712 0.0002686  0.20  1.00 6.6e-01
region.coal               -0.1678851 0.1845406 0.1790954  0.83  1.00 3.6e-01
region.postElec            0.3221044 0.1475714 0.1468522  4.76  1.00 2.9e-02
bank.polVote               0.0510398 0.9106599 0.7617459  0.00  1.00 9.6e-01
bank.roaLag1               0.0275158 0.1455080 0.1383035  0.04  1.00 8.5e-01
as.factor(region.govParty -0.1079697 0.3216745 0.2858614  0.11  1.00 7.4e-01
as.factor(region.govParty  0.7319720 0.3005609 0.2772277  5.93  1.00 1.5e-02
frailty(bank.id, distribu                                67.23 36.47 1.4e-03
as.factor(region.govParty -7.1981552 0.8567629 0.8565748 70.59  1.00 4.4e-17
as.factor(region.govParty -7.2222537 0.8562681 0.8560768 71.14  1.00 3.3e-17
as.factor(region.govParty -7.3081943 0.8564630 0.8562928 72.81  1.00 1.4e-17

                          exp(coef) exp(-coef) lower .95 upper .95
region.yrsInGov           1.0156958     0.9845 0.8366542  1.233052
I(region.yrsInGov^2)      0.9941932     1.0058 0.9752898  1.013463
I(region.yrsInGov^3)      1.0001202     0.9999 0.9995887  1.000652
region.coal               0.8454510     1.1828 0.5888534  1.213863
region.postElec           1.3800288     0.7246 1.0334159  1.842897
bank.polVote              1.0523648     0.9502 0.1766083  6.270778
bank.roaLag1              1.0278979     0.9729 0.7728467  1.367120
as.factor(region.govParty 0.8976548     1.1140 0.4778587  1.686239
as.factor(region.govParty 2.0791767     0.4810 1.1535952  3.747394
as.factor(region.govParty 0.0007480  1336.9621 0.0001395  0.004010
as.factor(region.govParty 0.0007302  1369.5722 0.0001363  0.003911
as.factor(region.govParty 0.0006700  1492.4798 0.0001250  0.003590

Iterations: 6 outer, 120 Newton-Raphson
     Variance of random effect= 0.3531767 
Degrees of freedom for terms=  1.0  1.0  1.0  0.9  1.0  0.7  0.9  1.6 36.5  2.8 
Concordance= 0.946  (se = 0.946 )
Likelihood ratio test= 815  on 47.39 df,   p=<2e-16


> screenreg(list(fit.main3, fit.quadratic, fit.cubic),
+   caption = "Cox PH Model, different polynomials for Years in Government", 
+   caption.above = T,
+   custom.coef.names = c("Years in Government"
+     , "Post Election"
+     , "Coalition"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist"
+     , "Years in Government^2", "Years in Government^3"),
+   reorder.coef = c(1, 8:9, 3, 2, 4:7),
+   custom.model.names = c("Linear", "Quadratic", "Cubic"),
+   include.rsquared=T, include.maxrs=T, include.loglik = T, 
+   include.missings=F, include.zph=F,
+   stars = c(.01,.05,.1),
+   omit.coef = c("bank.yrsInOffice"))

=============================================================
                          Linear       Quadratic   Cubic     
-------------------------------------------------------------
Years in Government         -0.06 ***    -0.02        0.02   
                            (0.02)       (0.05)      (0.10)  
Years in Government^2                    -0.00       -0.01   
                                         (0.00)      (0.01)  
Years in Government^3                                 0.00   
                                                     (0.00)  
Coalition                   -0.20        -0.17       -0.17   
                            (0.18)       (0.18)      (0.18)  
Post Election                0.28 **      0.31 **     0.32 **
                            (0.14)       (0.14)      (0.15)  
Public Sector Vote Share    -0.03         0.03        0.05   
                            (0.91)       (0.91)      (0.91)  
Return on Assets$_{t-1}$     0.04         0.03        0.03   
                            (0.15)       (0.15)      (0.15)  
Party: Other                -0.09        -0.11       -0.11   
                            (0.32)       (0.32)      (0.32)  
Party: Socialist             0.80 ***     0.74 **     0.73 **
                            (0.29)       (0.30)      (0.30)  
-------------------------------------------------------------
AIC                       2230.69      2232.34     2234.15   
R^2                          0.48         0.48        0.48   
Max. R^2                     0.90         0.90        0.90   
Num. events                234          234         234      
Num. obs.                 1259         1259        1259      
=============================================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> # **Figure 3**: plot different polynomials -----------------------------------
> 
> ## simulate values from Table A16, Column 1 using 'simPH' package
> plot.region.yrsInGov <- coxsimLinear(fit.main3
+   , b = "region.yrsInGov"
+   , Xj = seq(1, 28, by = 1)) # for year 1--28
All Xl set to 0.

> ## simulate values from Table A16, Column 2 using 'simPH' package
> plot.quadratic <- coxsimPoly(fit.quadratic, b = "region.yrsInGov", 
+   pow = 2, Xj = seq(1, 28, by = 1))
All Xl set at 0.

> ## simulate values from Table A16, Column 3 using 'simPH' package
> plot.cubic <- coxsimPoly(fit.cubic, b = "region.yrsInGov", 
+   pow = 3, Xj = seq(1, 28, by = 1))
All Xl set at 0.

> ## plot left-hand figure in Figure 3
> pdf("figures/effect_govChangeYr.pdf") 

> simGG(plot.region.yrsInGov
+   , xlab = "Years in Government"
+   , lcolour = "black"
+   , pcolour = "grey"
+   , alpha = .5
+   , type = "points")
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

> dev.off()
RStudioGD 
        2 

> ## plot middle figure in Figure 3
> pdf("figures/effect_quadratic.pdf") 

> simGG(plot.quadratic
+   , xlab = "Years in Government"
+   , lcolour = "black"
+   , pcolour = "grey"
+   , alpha = .5
+   , type = "points")
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

> dev.off()
RStudioGD 
        2 

> ## plot right-hand figure in Figure 3
> pdf("figures/effect_cubic.pdf") 

> simGG(plot.cubic
+   , xlab = "Years in Government"
+   , lcolour = "black"
+   , pcolour = "grey"
+   , alpha = .5
+   , type = "points")
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

> dev.off()
RStudioGD 
        2 

> # **Table A17**: Unseated coalition governments -------------------------------
> 
> ## Column 1
> fit.coal1 <- coxph(Surv(bank.yrsInOffice, bank.event) ~  region.coalBefore +
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & region.govChange == 1)

> summary(fit.coal1)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.coalBefore + 
    region.postElec + bank.polVote + bank.roaLag1 + as.factor(region.govParty) + 
    as.factor(region.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cajas.boards, subset = bank.chair == 
    1 & region.govChange == 1, method = "efron")

  n= 445, number of events= 94 

                          coef    se(coef) se2    Chisq DF    p      
region.coalBefore         -0.5084 0.2599   0.2455  3.83  1.00 5.0e-02
region.postElec            0.2762 0.2218   0.2207  1.55  1.00 2.1e-01
bank.polVote               1.2886 1.3070   1.1248  0.97  1.00 3.2e-01
bank.roaLag1              -0.2951 0.2510   0.2389  1.38  1.00 2.4e-01
as.factor(region.govParty  0.8048 0.4553   0.4224  3.13  1.00 7.7e-02
as.factor(region.govParty  0.2722 0.5709   0.5479  0.23  1.00 6.3e-01
frailty(bank.id, distribu                          9.22 15.94 9.0e-01
as.factor(region.govParty -8.0912 1.1935   1.1931 45.96  1.00 1.2e-11
as.factor(region.govParty -8.3579 1.1924   1.1921 49.13  1.00 2.4e-12
as.factor(region.govParty -8.3865 1.1931   1.1927 49.41  1.00 2.1e-12

                          exp(coef) exp(-coef) lower .95 upper .95
region.coalBefore         0.6014366     1.6627 3.614e-01  1.000921
region.postElec           1.3180770     0.7587 8.534e-01  2.035706
bank.polVote              3.6275667     0.2757 2.799e-01 47.007731
bank.roaLag1              0.7444285     1.3433 4.552e-01  1.217424
as.factor(region.govParty 2.2363032     0.4472 9.163e-01  5.458158
as.factor(region.govParty 1.3128168     0.7617 4.288e-01  4.019733
as.factor(region.govParty 0.0003062  3265.5552 2.952e-05  0.003176
as.factor(region.govParty 0.0002345  4263.6882 2.266e-05  0.002428
as.factor(region.govParty 0.0002279  4387.4337 2.199e-05  0.002363

Iterations: 10 outer, 200 Newton-Raphson
     Variance of random effect= 0.3219639 
Degrees of freedom for terms=  0.9  1.0  0.7  0.9  1.8 15.9  2.7 
Concordance= 0.932  (se = 0.932 )
Likelihood ratio test= 299.6  on 23.98 df,   p=<2e-16


> ## Column 2
> fit.coal2 <- coxph(Surv(bank.yrsInOffice, bank.event) ~ region.coalBefore +
+     region.postElec +
+     bank.polVote +
+     bank.roaLag1 +
+     as.factor(region.govParty) + as.factor(region.govParty):bank.yrsInOffice +
+     frailty(bank.id, distribution = "gaussian"),
+   method = "efron",
+   data = cajas.boards, subset = bank.chair == 1 & region.govChange == 0)

> summary(fit.coal2)
Call:
coxph(formula = Surv(bank.yrsInOffice, bank.event) ~ region.coalBefore + 
    region.postElec + bank.polVote + bank.roaLag1 + as.factor(region.govParty) + 
    as.factor(region.govParty):bank.yrsInOffice + frailty(bank.id, 
    distribution = "gaussian"), data = cajas.boards, subset = bank.chair == 
    1 & region.govChange == 0, method = "efron")

  n= 814, number of events= 140 

                          coef    se(coef) se2    Chisq DF    p      
region.coalBefore          0.5076 0.2648   0.2491  3.67  1.00 5.5e-02
region.postElec            0.4500 0.1827   0.1817  6.07  1.00 1.4e-02
bank.polVote              -1.4174 1.2080   1.0754  1.38  1.00 2.4e-01
bank.roaLag1               0.1087 0.1896   0.1798  0.33  1.00 5.7e-01
as.factor(region.govParty -0.5529 0.5093   0.4515  1.18  1.00 2.8e-01
as.factor(region.govParty  1.2514 0.4185   0.3712  8.94  1.00 2.8e-03
frailty(bank.id, distribu                         33.58 22.18 5.7e-02
as.factor(region.govParty -6.4442 1.4857   1.4856 18.81  1.00 1.4e-05
as.factor(region.govParty -6.4289 1.4855   1.4854 18.73  1.00 1.5e-05
as.factor(region.govParty -6.5555 1.4854   1.4854 19.48  1.00 1.0e-05

                          exp(coef) exp(-coef) lower .95 upper .95
region.coalBefore          1.661249     0.6020 9.886e-01   2.79166
region.postElec            1.568337     0.6376 1.096e+00   2.24346
bank.polVote               0.242342     4.1264 2.271e-02   2.58644
bank.roaLag1               1.114782     0.8970 7.688e-01   1.61644
as.factor(region.govParty  0.575267     1.7383 2.120e-01   1.56085
as.factor(region.govParty  3.495064     0.2861 1.539e+00   7.93673
as.factor(region.govParty  0.001590   629.0636 8.643e-05   0.02924
as.factor(region.govParty  0.001614   619.5130 8.780e-05   0.02968
as.factor(region.govParty  0.001422   703.1272 7.737e-05   0.02614

Iterations: 7 outer, 140 Newton-Raphson
     Variance of random effect= 0.2837345 
Degrees of freedom for terms=  0.9  1.0  0.8  0.9  1.6 22.2  2.9 
Concordance= 0.954  (se = 0.954 )
Likelihood ratio test= 508.5  on 30.21 df,   p=<2e-16


> screenreg(list(fit.coal1, fit.coal2),
+   caption = "Cox PH Model", caption.above = T,
+   custom.coef.names = c("Coalition in prev. term"
+     , "Post Election"
+     , "Public Sector Vote Share"
+     , "Return on Assets$_{t-1}$"
+     , "Party: Other", "Party: Socialist"),
+   custom.model.names = c("After Gov't Change", "No Gov't Change"),
+   include.rsquared=F, include.maxrs=F, include.missings=F, include.zph=F,
+   stars = c(.01,.05,.1),
+   omit.coef = c("bank.yrsInOffice"))

=============================================================
                          After Gov't Change  No Gov't Change
-------------------------------------------------------------
Coalition in prev. term    -0.51 *               0.51 *      
                           (0.26)               (0.26)       
Post Election               0.28                 0.45 **     
                           (0.22)               (0.18)       
Public Sector Vote Share    1.29                -1.42        
                           (1.31)               (1.21)       
Return on Assets$_{t-1}$   -0.30                 0.11        
                           (0.25)               (0.19)       
Party: Other                0.80 *              -0.55        
                           (0.46)               (0.51)       
Party: Socialist            0.27                 1.25 ***    
                           (0.57)               (0.42)       
-------------------------------------------------------------
AIC                       721.29              1220.01        
Num. events                94                  140           
Num. obs.                 445                  814           
=============================================================
*** p < 0.01, ** p < 0.05, * p < 0.1

> ### estimates in model 1 differ slightly from the submitted manuscript
> ### substantive conclusions are not affected
> ### this was discussed and accepted by the editor
